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[NO AUDIO]

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CATHY L. DRENNAN: So
welcome everybody.

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I hope you're really excited
about this semester coming

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up, and getting
involved with teaching.

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So I am Professor Cathy
Drennan, a professor

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here at MIT in the Chemistry
and Biology Department.

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And I want to start today--

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so this workshop is about
stereotype, stereotype threat,

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wise criticism,
unconscious bias.

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And I want to start, because
you're going to be teachers,

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with telling you
about my motivation

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for doing this training.

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I think she's a chemist.

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Why is she standing up
here telling us about this?

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So as teachers, it's good
to tell your students

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about your motivation.

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So what is my motivation?

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Well, my motivation was-- this
a story from a number of years

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ago, and I was asked to
attend a workshop happening

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at Harvard on increasing
diversity in the sciences.

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And so when you went to
register for this workshop

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they gave you a
homework assignment.

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I thought that was interesting.

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So my homework assignment was to
go to the chemistry department

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and interview all the
undergraduates that

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were members of underrepresented
minority groups.

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And I discovered at the time,
this was a number of years ago,

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there were two, and
both of these students

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had transferred very recently
to other departments.

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So that was not the
best indication,

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but the students were very
interested in talking to me.

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And I said, I want to hear
about your experience.

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I want to think about what the
department could do better.

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Again, this is an
issue from the past.

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Things are a lot
better right now.

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So I met with these students,
and one in particular

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was just really engaged with me.

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They're like, yes, I want
to help you figure out

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what's going on, and talking
about their experience,

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and thought a lot about what
could have been different.

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And finally, came
up with this thing,

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and he said, I never had
a TA that believed in me.

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And I just thought--

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I was not expecting
that response.

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Because I'd been
working with TAs at that

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point for quite some time.

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And the TAs were
really wonderful.

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They were engaged and excited.

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And I just thought,
somehow the TAs--

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there had been this disconnect.

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And I said, well, what
did the TAs say to you?

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What happened?

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And he thought
about it and tried

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to come up with an
example, and then realized,

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it wasn't what they said,
it was what they didn't say.

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And that just stuck with me.

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And I thought, his TAs
probably had no idea

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that he was feeling this way.

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Here was this kid sitting
in the classroom thinking,

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no one believes that
I can do chemistry,

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and no one else was thinking
that but him, probably.

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So I said, we need
to talk about this.

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We need to talk
about how we can all

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have classrooms where everyone
can reach their full potential.

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So that's what this
training is about today.

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We're going to start
this discussion, how

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do we create classrooms
where everyone can reach

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their full potential, where no
one's worried, sitting in there

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really worried,
about what someone

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might be thinking about them.

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So again, as a teacher, what
I'd like to do starting out

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is give you the
take home message.

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I like to give you
the take-home message,

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tell you what I'm
going to tell you,

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tell you the thing
I'm going to tell you,

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and then tell you
what I taught you.

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So there's a little bit of
teacher training mixed in

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with this workshop as well.

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All right, so take-home message.

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So the take-home message is
that understanding stereotype

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threat and wise criticism
is essential for being

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a good mentor,
supervisor, and teacher,

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and helps with being a good
human being, I think, as well.

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So that's the message.

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And you might think,
OK, I'm buying in,

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but I have no idea what you're
talking about with stereotype

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threat.

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So let's go there first.

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All right.

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So let's look at
some definitions.

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First, let's look at stereotype.

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So stereotype is a prevalent
belief about a specific type

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of individuals or a
way of doing things,

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which may or may
not reflect reality.

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So most people can
think of stereotypes.

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Stereotypes can be
positive or negative.

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Most people think of
negative stereotypes,

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but actually they
can be positive.

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So an example of a
positive stereotype

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is that MIT students are smart.

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I've been here almost 20
years, MIT students are smart.

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That's a positive stereotype.

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Stereotypes can
be based in truth.

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That is pretty true that
MIT students are smart.

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What about a
negative stereotype?

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What is an example of
a negative stereotype?

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Yes?

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AUDIENCE: So most
people see that jocks

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are dumb or not so bright.

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So that's an example
of negative stereotype.

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CATHY L. DRENNAN: Yes, and in
my experience, that's not true.

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There are a lot of
very athletic people--

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something that some people
don't know about MIT

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is that most people
participate in sports here.

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So yeah, so a negative one.

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Again, they can be positive,
they can be negative,

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they can be largely
true, or not so true.

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I also want to introduce the
idea of unconscious bias,

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because people are
talking a lot about this.

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And actually, when I
started this training,

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no one was talking about this
term, and now it's the term.

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So let's talk about that.

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So it's defined as
social stereotypes

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about certain groups that
individuals form outside

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of their conscious awareness.

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So in other words, they
don't know that they

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hold these stereotypes.

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Unconscious bias.

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And I think people
like this term

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because it's takes
the pressure off.

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I didn't know I have it,
therefore, I'm not responsible.

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It's all good.

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But today, we're really
getting in there,

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and I feel like I've been doing
this training for a while,

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so I've become acutely
aware of just how

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many stereotypes I have.

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So this is not about, oh, if
it were true, freeing ourselves

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of these things.

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No.

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It's about making ourselves
aware that we have them,

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and acting in such a way that
we can counter the harm of it.

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All right.

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So stereotype threat
is the perceived risk

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of confirming a
negative stereotype.

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So say, as a female driver
you do something stupid,

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and have a whole bunch
of men honking at you.

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Right?

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You're like, oh, I didn't
want to let the world--

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you feel pressure of doing
everything perfectly,

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because you don't want to play
into the negative idea there.

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All right.

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So that's what
stereotype threat is.

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And I think stereotype threat
is still a good term for it,

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because unconscious bias
threat doesn't really work.

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OK, so we're back
to stereotypes.

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All right.

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So what are we going
to talk about today?

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So we're going to talk
more about these terms.

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We're going to
talk about the fact

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that stereotype threat can
lead to underperformance.

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So I'm going to show you
data to support this idea.

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We're going to
talk about the fact

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that stereotype threat
can lead to the idea

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that you're being
judged unfairly.

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Sometimes you might
be judged unfairly,

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and sometimes you're not
being judged unfairly,

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but stereotype threat
can lead to that feeling

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whether or not it's true.

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Everyone can be a victim
of stereotype threat,

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and everyone has stereotypes.

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So it affects all of us.

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We all hold stereotypes,
we all can be

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victims of stereotype threat.

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This training is really
for everyone in the room.

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And I have to say, this
is not just about gender,

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it's not just about race.

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It's about anything that
might make us feel different,

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anything that might make
us feel under a microscope.

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If we're different in any way
from the others around us,

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this can affect everybody.

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So it's for everyone.

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This is the bad news.

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What about the good news?

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There's always good news.

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There's wise criticism.

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So this has been shown to
alleviate the negative aspects

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of stereotype threat.

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So what is it?

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So wise criticism is
criticism where you explicitly

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let somebody know
that they are capable,

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or you believe they're
capable, of a higher

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level of achievement.

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And there is data to suggest
this goes a long way.

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Again, if you can create
this environment of trust,

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if the individuals
in your classroom

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feel that you believe in
them, you can criticize them--

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I mean, this is important
in Mentoring Lab.

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Criticism is how we learn.

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When we don't do well,
we learn from that.

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It's all healthy and good.

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But we have to make sure that
any criticism is delivered

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in such a way that
the person recognizes

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that we believe in them.

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Again, back to the student
who never had a TA--

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we need everyone to know that
we believe that the person is

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able to do the work.

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And so, that is one way to
mitigate the negative effects.

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All right.

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So I don't know if I told
you this ahead of time,

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but this is an
interactive training.

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So we want everyone
to participate,

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and think about how this has
affected me or someone I know.

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So what we're going
to do in a minute

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is you're going to pair share.

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Find a group of two or three,
and turn to your neighbor

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and recall a time
when you might have

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felt judged by some
superficial characteristic.

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So it could be age,
it could be anything.

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So think about that or--

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and maybe and/or,
you can do both--

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you are worried about confirming
a negative stereotype.

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So that's the assignment.

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Think about it a little bit.

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You'll talk to your neighbor.

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I'll give you about
five minutes or so.

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And then we'll come
back in a group,

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and hopefully some of
you will feel comfortable

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sharing your experiences
with a larger group,

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and we can talk about it.

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All right.

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So let's take a few minutes,
find someone, share a story.

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AUDIENCE: So do you guys have
any experiences like that?

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AUDIENCE: Yeah, so
I guess one of mine

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that I started being really
aware of when I started

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grad school was that I was
really worried about being

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taken seriously as a scientist,
if I had a meaningful hobbies

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outside of work.

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AUDIENCE: So I guess for
me, when I came to MIT,

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I was really scared by
the fact that there were

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few people that looked like me.

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And so, that put like a
lot of pressure on me,

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and I felt that I had to
carry a whole race on my back.

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And also, I can
remember one meeting

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that I had with one
of the professors,

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and she was telling
me that, I know

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that you come from
a small college,

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and that your
classmates are going

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to be from Berkeley and
Harvard, and so it might be

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right for you at the beginning.

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And I feel that
really, really impacted

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how I performed in
some of my classes,

252
00:11:31,820 --> 00:11:35,930
and I wish that I had never
had that conversation.

253
00:11:35,930 --> 00:11:37,553
So, yeah.

254
00:11:37,553 --> 00:11:39,845
AUDIENCE: Yeah, being from
a smaller school was harder.

255
00:11:39,845 --> 00:11:41,840
I was also from
a smaller school,

256
00:11:41,840 --> 00:11:45,670
and in classes I was
always thinking--

257
00:11:45,670 --> 00:11:46,700
AUDIENCE: Yeah, yeah.

258
00:11:46,700 --> 00:11:48,650
Can I perform as
well as other people?

259
00:11:48,650 --> 00:11:50,040
AUDIENCE: As well.

260
00:11:50,040 --> 00:11:50,890
Mm-hmm.

261
00:11:50,890 --> 00:11:51,530
AUDIENCE: Yeah.

262
00:11:51,530 --> 00:11:53,730
AUDIENCE: And then being
told that you might not.

263
00:11:53,730 --> 00:11:54,905
AUDIENCE: Yeah, yeah, yeah.

264
00:11:54,905 --> 00:11:57,050
So yeah, so you
question yourself.

265
00:11:57,050 --> 00:12:00,620
Sometimes you feel like you
don't really belong here.

266
00:12:00,620 --> 00:12:03,170
And so, yeah, it can be
hard for some people.

267
00:12:03,170 --> 00:12:07,040
AUDIENCE: Yeah, I had a very
similar experience to yours.

268
00:12:07,040 --> 00:12:10,490
I was directly told that
students from Puerto Rico

269
00:12:10,490 --> 00:12:13,270
don't really do that well
in first year classes.

270
00:12:13,270 --> 00:12:17,160
And so, when I while I was
never doing terribly in class,

271
00:12:17,160 --> 00:12:21,630
I felt like I didn't have to
necessarily do well at all.

272
00:12:21,630 --> 00:12:25,960
So I would actually not try
as hard as I should have too.

273
00:12:25,960 --> 00:12:26,700
Just do well.

274
00:12:26,700 --> 00:12:29,600
AUDIENCE: Because that's what
they were expecting, right?

275
00:12:29,600 --> 00:12:31,955
AUDIENCE: Yeah.

276
00:12:31,955 --> 00:12:33,080
I think the other problem--

277
00:12:33,080 --> 00:12:35,730
I've heard many people that
have had the same experience.

278
00:12:35,730 --> 00:12:38,110
So it's sort of like--

279
00:12:38,110 --> 00:12:41,790
AUDIENCE: Yeah, I wish
that some of the professors

280
00:12:41,790 --> 00:12:45,890
would understand that they're
not generally helping you

281
00:12:45,890 --> 00:12:47,780
when saying these things.

282
00:12:47,780 --> 00:12:51,295
And maybe a training like
this wouldn't help them a lot.

283
00:12:51,295 --> 00:12:51,920
AUDIENCE: Yeah.

284
00:12:51,920 --> 00:12:54,890
AUDIENCE: Yeah, because were
they trying to-- they're

285
00:12:54,890 --> 00:12:56,690
coming from a place
of trying to help you.

286
00:12:56,690 --> 00:12:58,490
AUDIENCE: I think internally--

287
00:12:58,490 --> 00:13:02,250
yeah, I think their
intention was to help,

288
00:13:02,250 --> 00:13:03,650
but I think it's just--

289
00:13:03,650 --> 00:13:05,255
AUDIENCE: Misguided.

290
00:13:05,255 --> 00:13:05,880
AUDIENCE: Yeah.

291
00:13:05,880 --> 00:13:06,505
AUDIENCE: Yeah.

292
00:13:06,505 --> 00:13:07,250
AUDIENCE: Yeah.

293
00:13:07,250 --> 00:13:09,357
AUDIENCE: Yeah, it's true.

294
00:13:09,357 --> 00:13:10,940
AUDIENCE: So a time
that I was worried

295
00:13:10,940 --> 00:13:13,730
about confirming a negative
stereotype was when I first

296
00:13:13,730 --> 00:13:15,290
started graduate school.

297
00:13:15,290 --> 00:13:18,770
I was coming from a primarily
undergraduate institution.

298
00:13:18,770 --> 00:13:21,920
I had never been at a
big research university.

299
00:13:21,920 --> 00:13:24,080
And so, I didn't really
know what to expect

300
00:13:24,080 --> 00:13:26,180
or what I was
getting myself into.

301
00:13:26,180 --> 00:13:30,110
And I guess, when people
first explained to me

302
00:13:30,110 --> 00:13:33,440
what the expectations were
in terms of how much I should

303
00:13:33,440 --> 00:13:36,380
work, and what
vacation policies were,

304
00:13:36,380 --> 00:13:38,810
the first time they told me
I literally laughed out loud.

305
00:13:38,810 --> 00:13:41,210
I didn't think they could
possibly be serious.

306
00:13:41,210 --> 00:13:45,560
And then I realized, whoa,
I might be in over my head.

307
00:13:45,560 --> 00:13:48,580
And once I realized that,
I was like, oh, man.

308
00:13:48,580 --> 00:13:51,080
People are going to think that
I'm not serious about my job,

309
00:13:51,080 --> 00:13:52,413
and I don't know what I'm doing.

310
00:13:52,413 --> 00:13:55,370
So I had to pretend
all of a sudden

311
00:13:55,370 --> 00:13:57,920
that I was all focused on
science, because I didn't want

312
00:13:57,920 --> 00:14:00,230
people to think that I
wasn't taking it seriously

313
00:14:00,230 --> 00:14:01,880
because of my background.

314
00:14:01,880 --> 00:14:05,100
AUDIENCE: Yeah, no, I feel
the same way coming to MIT.

315
00:14:05,100 --> 00:14:07,190
Especially, coming
in for me, confirming

316
00:14:07,190 --> 00:14:09,590
the negative stereotype,
also just being

317
00:14:09,590 --> 00:14:12,560
judged for my physical
attributes as a woman of color.

318
00:14:12,560 --> 00:14:18,380
I feel like there's a lot of
stereotypes, negative, around

319
00:14:18,380 --> 00:14:19,718
my ethnicity and my race.

320
00:14:19,718 --> 00:14:21,760
And one of the things that
I heard a lot of times

321
00:14:21,760 --> 00:14:24,700
it's the idea that,
you speak well,

322
00:14:24,700 --> 00:14:26,990
or you're articulate
for a black woman.

323
00:14:26,990 --> 00:14:28,640
And this is not only my story.

324
00:14:28,640 --> 00:14:30,740
I've had a number
of my colleagues

325
00:14:30,740 --> 00:14:32,660
say the same thing to me.

326
00:14:32,660 --> 00:14:35,270
So it's something that's
just constantly being battled

327
00:14:35,270 --> 00:14:37,340
against a negative stereotype.

328
00:14:37,340 --> 00:14:40,010
And also this idea
that coming in--

329
00:14:40,010 --> 00:14:41,870
a combination of
my, I like to call

330
00:14:41,870 --> 00:14:45,450
it an intersectionality
of all my personalities

331
00:14:45,450 --> 00:14:46,940
and all my identities.

332
00:14:46,940 --> 00:14:48,680
So I'm a woman of color.

333
00:14:48,680 --> 00:14:49,820
I am from the Caribbean.

334
00:14:49,820 --> 00:14:51,645
I went to a small
school, but I'm not only

335
00:14:51,645 --> 00:14:53,020
just a small
liberal arts school,

336
00:14:53,020 --> 00:14:55,850
I went to a small HBCU, which
is a historically Black college

337
00:14:55,850 --> 00:14:56,570
and university.

338
00:14:56,570 --> 00:14:59,540
And I feel like all those
combinations also mix.

339
00:14:59,540 --> 00:15:01,100
Get into a classroom,
and being one

340
00:15:01,100 --> 00:15:03,650
of the only persons
of color makes

341
00:15:03,650 --> 00:15:05,548
me feel extra pressure
for confirming

342
00:15:05,548 --> 00:15:07,340
these negative stereotypes
that are thought

343
00:15:07,340 --> 00:15:09,470
against people of color.

344
00:15:09,470 --> 00:15:11,713
So in the classrooms I
try not to ask questions,

345
00:15:11,713 --> 00:15:13,130
because I don't
want to out myself

346
00:15:13,130 --> 00:15:17,467
as not being smart, or not as
much as my other colleagues

347
00:15:17,467 --> 00:15:19,050
who might have went
to bigger schools,

348
00:15:19,050 --> 00:15:22,470
and have had targeted classes
on this particular subject,

349
00:15:22,470 --> 00:15:23,420
for example.

350
00:15:23,420 --> 00:15:27,460
So it's been definitely the idea
of being the "face of the race"

351
00:15:27,460 --> 00:15:30,100
also, because you don't want to
spoil it for other people who

352
00:15:30,100 --> 00:15:30,670
come along.

353
00:15:30,670 --> 00:15:32,680
So it's been a
little bit like you

354
00:15:32,680 --> 00:15:35,800
feel like you have
to not ask questions,

355
00:15:35,800 --> 00:15:38,050
you're not out yourself.

356
00:15:38,050 --> 00:15:40,990
Anything you say might prove
that negative stereotype,

357
00:15:40,990 --> 00:15:43,930
and it's a lot of
pressure to do.

358
00:15:43,930 --> 00:15:44,430
So yeah.

359
00:15:44,430 --> 00:15:46,180
AUDIENCE: Yeah, totally.

360
00:15:46,180 --> 00:15:48,940
AUDIENCE: I can't necessarily
relate to your experience,

361
00:15:48,940 --> 00:15:52,360
but the flip side of
the coin on that is I

362
00:15:52,360 --> 00:15:53,867
do have one sliver
of an experience

363
00:15:53,867 --> 00:15:55,450
where I kind of feel
like I can relate

364
00:15:55,450 --> 00:15:56,658
to what you're talking about.

365
00:15:56,658 --> 00:15:59,270
And that's when we're having
conversations about diversity,

366
00:15:59,270 --> 00:16:01,330
I feel like I don't
want to say something

367
00:16:01,330 --> 00:16:04,900
that might sound ignorant,
or naive, or uninformed.

368
00:16:04,900 --> 00:16:07,900
Because I feel like if I end
up saying something stupid

369
00:16:07,900 --> 00:16:11,510
and offending someone, I set
the conversation back a step,

370
00:16:11,510 --> 00:16:14,020
and then I end up
confirming the stereotype

371
00:16:14,020 --> 00:16:17,620
that all white men don't care
about improving diversity

372
00:16:17,620 --> 00:16:18,490
in science.

373
00:16:18,490 --> 00:16:20,650
AUDIENCE: Yeah, well,
that's very understandable.

374
00:16:20,650 --> 00:16:21,970
And I feel like it's
very crippling too,

375
00:16:21,970 --> 00:16:24,440
because he might have really
good experiences that you want

376
00:16:24,440 --> 00:16:26,440
to share, and good
contributions that you

377
00:16:26,440 --> 00:16:28,080
could make in that sense.

378
00:16:28,080 --> 00:16:30,770
And you can't do it because
of that negative stereotype.

379
00:16:30,770 --> 00:16:32,020
You're really confirming that.

380
00:16:32,020 --> 00:16:34,270
So it's definitely
very crippling.

381
00:16:34,270 --> 00:16:37,900
AUDIENCE: Yeah, I think it is
detrimental to the whole cause,

382
00:16:37,900 --> 00:16:39,515
because it takes
everybody, right?

383
00:16:39,515 --> 00:16:40,640
AUDIENCE: It does, it does.

384
00:16:40,640 --> 00:16:41,973
AUDIENCE: For all participating.

385
00:16:41,973 --> 00:16:43,510
AUDIENCE: It is.

386
00:16:43,510 --> 00:16:46,240
AUDIENCE: OK, so I think--

387
00:16:46,240 --> 00:16:46,740
let me see.

388
00:16:46,740 --> 00:16:48,115
Thinking about a
time when I felt

389
00:16:48,115 --> 00:16:50,020
judged about a
negative stereotype.

390
00:16:50,020 --> 00:16:52,540
So I'm pretty short.

391
00:16:52,540 --> 00:16:55,720
I don't know if you noticed when
I walked in, but I think that--

392
00:16:55,720 --> 00:16:56,785
and I look pretty young.

393
00:16:56,785 --> 00:16:58,660
My sister's two years
younger, but I'm always

394
00:16:58,660 --> 00:17:01,720
considered the
younger sister, and I

395
00:17:01,720 --> 00:17:04,569
think that that's
led me to always

396
00:17:04,569 --> 00:17:08,710
be really afraid of confirming
that women are weak,

397
00:17:08,710 --> 00:17:11,450
or not able to do
things on their own.

398
00:17:11,450 --> 00:17:17,050
So I'm very conscious
of not asking for help

399
00:17:17,050 --> 00:17:18,190
with certain situations.

400
00:17:18,190 --> 00:17:20,140
If I can't reach things
or things like that,

401
00:17:20,140 --> 00:17:21,760
I know where all
the step-stools are.

402
00:17:21,760 --> 00:17:26,740
Or I've been known to climb
on shelves in grocery stores

403
00:17:26,740 --> 00:17:28,150
instead of asking for help.

404
00:17:28,150 --> 00:17:31,360
And it's one of those situations
where I'm very much afraid,

405
00:17:31,360 --> 00:17:34,030
and I often lead with the
fact that I have a black belt

406
00:17:34,030 --> 00:17:37,870
in Taekwondo to
kind of help paint

407
00:17:37,870 --> 00:17:41,230
this picture that I actually
can do things for myself.

408
00:17:41,230 --> 00:17:42,063
So, yeah.

409
00:17:42,063 --> 00:17:43,480
AUDIENCE: I feel
that, because I'm

410
00:17:43,480 --> 00:17:45,340
literally the same way,
including the black belt.

411
00:17:45,340 --> 00:17:45,840
(LAUGHS)

412
00:17:45,840 --> 00:17:46,470
AUDIENCE: Yeah.

413
00:17:46,470 --> 00:17:48,040
AUDIENCE: It's one
thing I lead with.

414
00:17:48,040 --> 00:17:50,017
AUDIENCE: It's
something that kind

415
00:17:50,017 --> 00:17:52,100
of shocks people, because
they're like, oh, you're

416
00:17:52,100 --> 00:17:53,918
so little.

417
00:17:53,918 --> 00:17:54,460
I don't know.

418
00:17:54,460 --> 00:17:56,590
I feel like I'm always
so worried about the fact

419
00:17:56,590 --> 00:17:58,090
that just because
I'm so little--

420
00:17:58,090 --> 00:17:59,465
and I've always
been this little,

421
00:17:59,465 --> 00:18:02,530
it's not like I was the
tallest person in my class--

422
00:18:02,530 --> 00:18:05,960
that I need to be able to show
that I can take care of myself,

423
00:18:05,960 --> 00:18:10,330
and be independent, because
of this stereotype that women

424
00:18:10,330 --> 00:18:13,220
can't, and need a
man to make sure

425
00:18:13,220 --> 00:18:15,160
that things are OK for them.

426
00:18:15,160 --> 00:18:16,090
So, yeah.

427
00:18:16,090 --> 00:18:17,590
That's where I'm at with that.

428
00:18:17,590 --> 00:18:19,750
AUDIENCE: Oh, I'm sorry.

429
00:18:19,750 --> 00:18:20,500
AUDIENCE: It's OK.

430
00:18:20,500 --> 00:18:21,660
I can't get taller.

431
00:18:21,660 --> 00:18:24,890
AUDIENCE: Yeah, well,
when I was six years old,

432
00:18:24,890 --> 00:18:27,430
I actually used to race cars.

433
00:18:27,430 --> 00:18:29,830
And I remember
there was a day when

434
00:18:29,830 --> 00:18:32,290
I should've gotten
second place in a race,

435
00:18:32,290 --> 00:18:35,020
but because the three
boys in front of me

436
00:18:35,020 --> 00:18:38,890
crashed right before the end of
the race, they gave me fifth,

437
00:18:38,890 --> 00:18:42,160
because they were in
second and third and fourth

438
00:18:42,160 --> 00:18:44,410
before the flag went down.

439
00:18:44,410 --> 00:18:45,850
But then I crossed
the line first,

440
00:18:45,850 --> 00:18:47,142
so I should have gotten second.

441
00:18:47,142 --> 00:18:49,930
Anyway, after this
whole situation is over,

442
00:18:49,930 --> 00:18:52,810
one of the boys came
up to me and was like,

443
00:18:52,810 --> 00:18:54,610
oh, well, you're a girl.

444
00:18:54,610 --> 00:18:56,440
Of course you can't
get second place.

445
00:18:56,440 --> 00:18:58,690
And I was like, excuse me?

446
00:18:58,690 --> 00:19:00,572
Yes, that was over 20 years ago.

447
00:19:00,572 --> 00:19:02,280
AUDIENCE: And that
was when you were six.

448
00:19:02,280 --> 00:19:03,160
AUDIENCE: That was
when I was six.

449
00:19:03,160 --> 00:19:04,285
AUDIENCE: How old was the--

450
00:19:04,285 --> 00:19:06,820
AUDIENCE: I think he was seven.

451
00:19:06,820 --> 00:19:07,990
So, yeah.

452
00:19:07,990 --> 00:19:08,890
It's still there.

453
00:19:08,890 --> 00:19:10,290
So, yeah.

454
00:19:10,290 --> 00:19:10,901
Good times.

455
00:19:10,901 --> 00:19:12,343
AUDIENCE: Yeah.

456
00:19:12,343 --> 00:19:13,510
CATHY L. DRENNAN: All right.

457
00:19:13,510 --> 00:19:14,962
Let's come back as a group.

458
00:19:14,962 --> 00:19:16,420
There was a lot of
good discussion.

459
00:19:16,420 --> 00:19:20,040
I can't wait to hear
what you're going to say.

460
00:19:20,040 --> 00:19:22,780
But I thought I would start
out by sharing a story

461
00:19:22,780 --> 00:19:24,510
to get us going.

462
00:19:24,510 --> 00:19:28,390
So in the first category, a
time that you were judged.

463
00:19:28,390 --> 00:19:31,510
So I think back to
a time when I was

464
00:19:31,510 --> 00:19:34,300
graduating from Vassar College.

465
00:19:34,300 --> 00:19:38,290
These are the old days, where if
you announced who got an award,

466
00:19:38,290 --> 00:19:41,860
they'd post a piece of
paper in the student union.

467
00:19:41,860 --> 00:19:44,800
There was no email thing
to send out to people.

468
00:19:44,800 --> 00:19:46,890
And so, everyone
gathered around the list

469
00:19:46,890 --> 00:19:49,540
of people who had been
named Phi Beta Kappa.

470
00:19:49,540 --> 00:19:53,230
And I was really excited that I
had been named Phi Beta Kappa.

471
00:19:53,230 --> 00:19:56,638
And so, I'm leaving after
having looked at the list,

472
00:19:56,638 --> 00:19:58,180
and I saw someone
who I hadn't really

473
00:19:58,180 --> 00:19:59,630
talked to since freshman year.

474
00:19:59,630 --> 00:20:01,810
He was in my dorm, and it
was one of those things

475
00:20:01,810 --> 00:20:04,990
like freshman year, where
the entire floor of your dorm

476
00:20:04,990 --> 00:20:06,820
like goes everywhere together.

477
00:20:06,820 --> 00:20:08,680
You go to the dining
hall together,

478
00:20:08,680 --> 00:20:11,320
you go to parties
together, because there's

479
00:20:11,320 --> 00:20:12,100
this herd effect.

480
00:20:12,100 --> 00:20:15,040
Anyway, this guy Phillip
had been in my herd,

481
00:20:15,040 --> 00:20:17,380
but I'd never actually
talked to him.

482
00:20:17,380 --> 00:20:21,400
So he walks up to me and he
looks at me, and he said,

483
00:20:21,400 --> 00:20:24,480
you were on that list.

484
00:20:24,480 --> 00:20:28,990
And I said, yes.

485
00:20:28,990 --> 00:20:33,240
And he just looks at
me, and then he said,

486
00:20:33,240 --> 00:20:39,050
it never occurred to
me that you were smart.

487
00:20:39,050 --> 00:20:41,430
[SOFT LAUGHTER]

488
00:20:41,430 --> 00:20:44,760
I mean you could just
see the wheels turning

489
00:20:44,760 --> 00:20:49,700
like, her name is on the list!

490
00:20:49,700 --> 00:20:53,330
Her name is on that list!

491
00:20:53,330 --> 00:20:56,070
She's smart?

492
00:20:56,070 --> 00:20:59,660
And I just thought like, what
did I do that this was such--

493
00:20:59,660 --> 00:21:00,680
It's like, oh, whatever.

494
00:21:00,680 --> 00:21:02,680
If they saw it, they might
not have ever thought

495
00:21:02,680 --> 00:21:04,550
about it one way or
another, but clearly, he

496
00:21:04,550 --> 00:21:09,080
had formed an extremely strong
opinion that there was no way

497
00:21:09,080 --> 00:21:11,990
that I could be smart,
and this was just

498
00:21:11,990 --> 00:21:15,710
messing with his worldview, the
fact my name was on the list.

499
00:21:15,710 --> 00:21:17,930
So what was it about
me that was just

500
00:21:17,930 --> 00:21:22,280
so inconceivable to this
person that I could be smart?

501
00:21:22,280 --> 00:21:29,000
OK, so in the second category,
pretend you're at MIT.

502
00:21:29,000 --> 00:21:32,270
In the Chemistry Department at
the point where I was coming up

503
00:21:32,270 --> 00:21:34,580
for tenure, there was
one female professor

504
00:21:34,580 --> 00:21:36,560
who had gotten
tenure, and then they

505
00:21:36,560 --> 00:21:38,660
had hired two more
in with tenure.

506
00:21:38,660 --> 00:21:40,310
So I was number two.

507
00:21:40,310 --> 00:21:41,570
Case study.

508
00:21:41,570 --> 00:21:43,130
Would she get tenure?

509
00:21:43,130 --> 00:21:44,780
And I felt like they
were all watching.

510
00:21:44,780 --> 00:21:46,670
Is she going to meetings?

511
00:21:46,670 --> 00:21:49,160
How many papers
has she published?

512
00:21:49,160 --> 00:21:50,180
Is she going to make it?

513
00:21:50,180 --> 00:21:50,930
What do you think?

514
00:21:50,930 --> 00:21:51,860
Lay in the bets.

515
00:21:51,860 --> 00:21:52,640
What are the odds?

516
00:21:52,640 --> 00:21:53,120
I don't know.

517
00:21:53,120 --> 00:21:54,110
There probably were odds.

518
00:21:54,110 --> 00:21:55,235
I don't know what they are.

519
00:21:55,235 --> 00:21:56,750
I never want to know.

520
00:21:56,750 --> 00:22:00,350
But I just felt like,
man, my not getting tenure

521
00:22:00,350 --> 00:22:03,170
it's not really
about me anymore.

522
00:22:03,170 --> 00:22:06,410
It's, can women get tenure?

523
00:22:06,410 --> 00:22:08,610
And it was all riding
on my shoulders.

524
00:22:08,610 --> 00:22:10,430
I was going to
provide the answer,

525
00:22:10,430 --> 00:22:12,552
and people would make
decisions about their lives,

526
00:22:12,552 --> 00:22:14,260
I was afraid, based
on whether it worked.

527
00:22:14,260 --> 00:22:15,385
Oh, it didn't work for her.

528
00:22:15,385 --> 00:22:17,750
I'm not sure I
want to go forward.

529
00:22:17,750 --> 00:22:19,220
That is a lot of
pressure when you

530
00:22:19,220 --> 00:22:24,540
feel like you're carrying other
people besides yourself around.

531
00:22:24,540 --> 00:22:26,160
So that's an example for me.

532
00:22:26,160 --> 00:22:26,660
All right.

533
00:22:26,660 --> 00:22:29,780
So you don't have to have
as profound examples,

534
00:22:29,780 --> 00:22:32,090
but everyone has some
Phillip in their life, right?

535
00:22:32,090 --> 00:22:33,260
Has somebody like that.

536
00:22:33,260 --> 00:22:35,170
So let's hear.

537
00:22:35,170 --> 00:22:39,194
Who wants to share about this?

538
00:22:39,194 --> 00:22:40,423
Yeah?

539
00:22:40,423 --> 00:22:42,340
AUDIENCE: So in addition
to being a scientist,

540
00:22:42,340 --> 00:22:45,730
I'm also a musician, and
when I got to grad school

541
00:22:45,730 --> 00:22:48,400
I felt really insecure
about having this hobby,

542
00:22:48,400 --> 00:22:53,410
and thought that I might not be
seen as serious of a scientist

543
00:22:53,410 --> 00:22:56,390
because I like to
play music as well.

544
00:22:56,390 --> 00:23:00,560
And so, I downplayed my hobby,
and didn't join ensembles

545
00:23:00,560 --> 00:23:01,060
as much.

546
00:23:01,060 --> 00:23:03,310
But even then, when I
started in grad school

547
00:23:03,310 --> 00:23:04,870
participating in
an ensemble, I was

548
00:23:04,870 --> 00:23:06,790
really worried I'd
confirm the stereotype

549
00:23:06,790 --> 00:23:09,880
that just because I
was also a musician,

550
00:23:09,880 --> 00:23:11,833
I was less serious
of a scientist.

551
00:23:11,833 --> 00:23:14,000
CATHY L. DRENNAN: Yeah,
thanks for bringing that up.

552
00:23:14,000 --> 00:23:16,910
I think that's a
really great example.

553
00:23:16,910 --> 00:23:20,080
So it's just we
have these ideas,

554
00:23:20,080 --> 00:23:25,780
and it's like we have these
square holes and round holes,

555
00:23:25,780 --> 00:23:28,930
and we're trying to
bat everybody into it.

556
00:23:28,930 --> 00:23:30,970
There's many different
things that people

557
00:23:30,970 --> 00:23:32,260
can do if they're scientists.

558
00:23:32,260 --> 00:23:34,840
There's many different
hobbies and other interests

559
00:23:34,840 --> 00:23:36,340
that they can have,
and we shouldn't

560
00:23:36,340 --> 00:23:38,500
have such a narrow definition.

561
00:23:38,500 --> 00:23:39,865
So someone else?

562
00:23:39,865 --> 00:23:41,680
I heard lots of
discussion going on.

563
00:23:41,680 --> 00:23:42,730
Yeah?

564
00:23:42,730 --> 00:23:45,220
AUDIENCE: So in
terms of being judged

565
00:23:45,220 --> 00:23:49,280
by someone for a superficial
characteristic, as an African--

566
00:23:49,280 --> 00:23:50,860
Afro-Caribbean I should say--

567
00:23:50,860 --> 00:23:55,090
woman in the scientific
setting, I feel like one thing

568
00:23:55,090 --> 00:23:59,260
I get a lot is that,
you speak very well.

569
00:23:59,260 --> 00:24:01,840
And usually I'm like, for?

570
00:24:01,840 --> 00:24:03,550
Why?

571
00:24:03,550 --> 00:24:07,170
But I think it's definitely
I'm clearly a minority,

572
00:24:07,170 --> 00:24:12,670
so basically there's
some negative stereotypes

573
00:24:12,670 --> 00:24:16,070
about whole women of color
should present themselves,

574
00:24:16,070 --> 00:24:18,380
and I don't fit in
to that stereotype.

575
00:24:18,380 --> 00:24:23,230
So every time I do give a talk
or I do express my opinions,

576
00:24:23,230 --> 00:24:25,090
people say I'm very well-spoken.

577
00:24:25,090 --> 00:24:28,480
And this is not
only my own case.

578
00:24:28,480 --> 00:24:29,900
I've heard a number
of my friends

579
00:24:29,900 --> 00:24:32,530
say the same things, even
sometimes from their advisors

580
00:24:32,530 --> 00:24:35,390
and people who have gone
through a lot of students.

581
00:24:35,390 --> 00:24:39,310
So that's one case
where I feel like I've

582
00:24:39,310 --> 00:24:43,868
been judged superficially for
a superficial characteristic.

583
00:24:43,868 --> 00:24:45,910
Another thing of confirming
a negative stereotype

584
00:24:45,910 --> 00:24:47,540
also comes back to my ethnicity.

585
00:24:51,240 --> 00:24:56,590
You mentioned feeling like you
carry the weight of persons

586
00:24:56,590 --> 00:25:00,130
on your back, and
thinking about being,

587
00:25:00,130 --> 00:25:02,320
again, an African-American
woman, most of the time

588
00:25:02,320 --> 00:25:04,720
the only one in a room.

589
00:25:04,720 --> 00:25:05,220
Not today.

590
00:25:05,220 --> 00:25:06,690
[LAUGHTER]

591
00:25:06,690 --> 00:25:07,345
That's great.

592
00:25:07,345 --> 00:25:09,720
But most of the time it's
being the only one in the room.

593
00:25:09,720 --> 00:25:12,250
And especially coming
from a small college,

594
00:25:12,250 --> 00:25:14,650
I also felt coming into my
first chemistry classes,

595
00:25:14,650 --> 00:25:17,170
there was this pressure
that I put on myself to not

596
00:25:17,170 --> 00:25:20,047
confirm these negative
stereotypes about people

597
00:25:20,047 --> 00:25:21,880
of color in terms of
that they're not smart,

598
00:25:21,880 --> 00:25:23,680
or they're not good
at math or science,

599
00:25:23,680 --> 00:25:25,310
and I didn't belong there.

600
00:25:25,310 --> 00:25:29,170
And it's not because people
around me said anything.

601
00:25:29,170 --> 00:25:30,940
It was definitely
internal that I

602
00:25:30,940 --> 00:25:33,650
felt like I had to
not answer a question,

603
00:25:33,650 --> 00:25:37,060
or not ask a question because
I didn't want to look as if I

604
00:25:37,060 --> 00:25:39,940
didn't know something,
or all my classmates

605
00:25:39,940 --> 00:25:41,560
knew it and I didn't know it.

606
00:25:41,560 --> 00:25:44,170
So it's that weight
of one, not confirming

607
00:25:44,170 --> 00:25:46,270
that negative stereotype,
and also not spoiling it

608
00:25:46,270 --> 00:25:48,640
for everybody else
who comes after me

609
00:25:48,640 --> 00:25:51,867
who is a URM from an HBCU.

610
00:25:51,867 --> 00:25:52,825
CATHY L. DRENNAN: Yeah.

611
00:25:52,825 --> 00:25:54,370
Those are both really
great examples.

612
00:25:54,370 --> 00:25:56,350
Thank you for sharing those.

613
00:25:56,350 --> 00:25:58,720
I think that really gets
to the heart of what

614
00:25:58,720 --> 00:26:01,990
we're trying to talk about.

615
00:26:01,990 --> 00:26:06,858
Feeling judged, and you
get these compliments--

616
00:26:06,858 --> 00:26:08,650
and I've gotten this
for some of the talks.

617
00:26:08,650 --> 00:26:12,220
It's like, that was
actually pretty good!

618
00:26:12,220 --> 00:26:13,970
What was your expectation
ahead of time?

619
00:26:13,970 --> 00:26:15,610
Or, you spoke well!

620
00:26:15,610 --> 00:26:19,790
Like, thank you.

621
00:26:19,790 --> 00:26:26,410
Yeah, and that
extra pressure to--

622
00:26:26,410 --> 00:26:28,040
who else is coming
along with us?

623
00:26:28,040 --> 00:26:29,470
So thank you for sharing those.

624
00:26:29,470 --> 00:26:31,540
That's really great.

625
00:26:31,540 --> 00:26:32,235
Maybe one more.

626
00:26:32,235 --> 00:26:34,360
Does someone else have
something you want to share?

627
00:26:34,360 --> 00:26:36,280
Yeah?

628
00:26:36,280 --> 00:26:38,530
AUDIENCE: When I
participate in conversations

629
00:26:38,530 --> 00:26:41,140
about improving
diversity in science,

630
00:26:41,140 --> 00:26:45,070
a lot of times I'll worry that
if I ask an uninformed question

631
00:26:45,070 --> 00:26:49,000
or make a naive suggestion that
I'll set the conversation back

632
00:26:49,000 --> 00:26:51,250
or offend somebody,
and ultimately

633
00:26:51,250 --> 00:26:54,880
confirm the stereotype that
white male scientists aren't

634
00:26:54,880 --> 00:26:57,250
serious about improving
diversity in science.

635
00:26:57,250 --> 00:26:59,290
So then I'll tell
myself, just sit back

636
00:26:59,290 --> 00:27:00,905
and listen to other people.

637
00:27:00,905 --> 00:27:02,530
And then as soon as
I start doing that,

638
00:27:02,530 --> 00:27:05,350
I'll start to worry, how
are people perceiving this?

639
00:27:05,350 --> 00:27:08,020
Do I look sullen and like
I don't care about this?

640
00:27:08,020 --> 00:27:09,670
And then, again,
I worry that I'm

641
00:27:09,670 --> 00:27:11,140
confirming a
different stereotype

642
00:27:11,140 --> 00:27:12,380
about white men in science.

643
00:27:12,380 --> 00:27:12,405
[SOFT LAUGHTER]

644
00:27:12,405 --> 00:27:14,620
So I think ultimately,
it's important for me

645
00:27:14,620 --> 00:27:19,960
to remember that my voice is
overrepresented in science,

646
00:27:19,960 --> 00:27:23,290
and I should make a concerted
effort to actively listen

647
00:27:23,290 --> 00:27:26,350
to other people's
perspectives, but also

648
00:27:26,350 --> 00:27:29,553
recognize that by spending so
much mental energy worrying

649
00:27:29,553 --> 00:27:31,470
about how other people
might be perceiving me,

650
00:27:31,470 --> 00:27:35,260
I'm ultimately undermining
my own ability to make

651
00:27:35,260 --> 00:27:38,710
a positive contribution towards
improving diversity in science.

652
00:27:38,710 --> 00:27:40,585
CATHY L. DRENNAN: Thank
you for sharing that.

653
00:27:40,585 --> 00:27:43,150
I think that's a
really good point.

654
00:27:43,150 --> 00:27:47,830
I've had people, when I've done
this training before, a man say

655
00:27:47,830 --> 00:27:51,550
that he felt very much under
sort of stereotype threat

656
00:27:51,550 --> 00:27:53,300
when he was taking a
woman's study course,

657
00:27:53,300 --> 00:27:55,330
and he was the only
guy in the class.

658
00:27:55,330 --> 00:27:57,940
And that sometimes he
felt like if he spoke up

659
00:27:57,940 --> 00:28:00,160
and what he said, that
he was going to really

660
00:28:00,160 --> 00:28:04,710
be judged by it, and it was just
a very uncomfortable situation.

661
00:28:04,710 --> 00:28:07,180
And I really feel like
what we want to do here

662
00:28:07,180 --> 00:28:08,350
is start the discussion.

663
00:28:08,350 --> 00:28:10,780
Everyone's voice
is so important.

664
00:28:10,780 --> 00:28:12,400
And sometimes it
drives me crazy when

665
00:28:12,400 --> 00:28:15,160
I'm hearing about women
in science, it's like, OK,

666
00:28:15,160 --> 00:28:16,540
you, woman, go fix this.

667
00:28:16,540 --> 00:28:18,080
It's like, I can't do this.

668
00:28:18,080 --> 00:28:20,620
I think we all need to
be talking about this

669
00:28:20,620 --> 00:28:21,850
and doing this together.

670
00:28:21,850 --> 00:28:24,520
These problems are not
problems for women to solve,

671
00:28:24,520 --> 00:28:26,440
or for people of color to solve.

672
00:28:26,440 --> 00:28:28,210
We want to improve science.

673
00:28:28,210 --> 00:28:30,040
We all need to be part
of this discussion,

674
00:28:30,040 --> 00:28:32,230
and everyone's voice
is so important.

675
00:28:32,230 --> 00:28:35,500
So we want to have a
situation where everyone feels

676
00:28:35,500 --> 00:28:37,780
like they can
speak, and everyone

677
00:28:37,780 --> 00:28:40,060
is going to listen to what
other people have to say.

678
00:28:40,060 --> 00:28:41,800
So very on point.

679
00:28:41,800 --> 00:28:43,010
Thank you very much.

680
00:28:43,010 --> 00:28:43,510
All right.

681
00:28:43,510 --> 00:28:46,840
So I think we should
get back, and we

682
00:28:46,840 --> 00:28:49,480
want to go back because I
have some data to show you.

683
00:28:49,480 --> 00:28:51,710
And this is MIT, and
we're scientists,

684
00:28:51,710 --> 00:28:54,080
so let's look at some data.

685
00:28:54,080 --> 00:28:54,580
All right.

686
00:28:54,580 --> 00:28:58,450
So there is a book by
Claude Steele called

687
00:28:58,450 --> 00:29:00,910
Whistling Vivaldi, and
Other Clues as to How

688
00:29:00,910 --> 00:29:03,070
Stereotypes Affect Us.

689
00:29:03,070 --> 00:29:06,460
Claude Steele is often viewed
as the father of stereotype

690
00:29:06,460 --> 00:29:09,490
threat, really coming
up with this concept

691
00:29:09,490 --> 00:29:11,620
and making people aware of it.

692
00:29:11,620 --> 00:29:14,095
And his book has a
lot of data in it.

693
00:29:14,095 --> 00:29:15,220
A lot of different studies.

694
00:29:15,220 --> 00:29:18,460
He summarizes studies that many
different people have done.

695
00:29:18,460 --> 00:29:23,620
So some of the studies are more
social studies, some of them

696
00:29:23,620 --> 00:29:26,260
are more in the
neuroscience area.

697
00:29:26,260 --> 00:29:29,920
So some studies that
he describes actually

698
00:29:29,920 --> 00:29:33,880
look at brain waves and
blood flow in the brain.

699
00:29:33,880 --> 00:29:36,970
And so, they will put people
under stereotype threat,

700
00:29:36,970 --> 00:29:39,910
and there's a variety
of ways you can do it,

701
00:29:39,910 --> 00:29:42,850
poking at those
negative stereotypes

702
00:29:42,850 --> 00:29:47,350
that we talked about, and
then actually watch blood flow

703
00:29:47,350 --> 00:29:48,790
or brain activity.

704
00:29:48,790 --> 00:29:52,270
And you can see movement
from the part of the brain

705
00:29:52,270 --> 00:29:54,370
where it's about
logic and reasoning,

706
00:29:54,370 --> 00:29:56,320
to, when people are
under stereotype

707
00:29:56,320 --> 00:30:00,310
threat, the part of the brain
that's about fear, about fight

708
00:30:00,310 --> 00:30:01,610
or flight.

709
00:30:01,610 --> 00:30:04,360
And so when you're
in the classroom,

710
00:30:04,360 --> 00:30:07,640
the goal is all the students
their brain is with logic,

711
00:30:07,640 --> 00:30:08,140
right?

712
00:30:08,140 --> 00:30:09,970
They're processing
the information

713
00:30:09,970 --> 00:30:11,290
and they're learning.

714
00:30:11,290 --> 00:30:12,940
You don't want people
in your classroom

715
00:30:12,940 --> 00:30:17,020
where the brain is very
anxious, and worrying about

716
00:30:17,020 --> 00:30:19,400
whether you belong and
whether you can do it.

717
00:30:19,400 --> 00:30:23,110
So there's an actual biological
reason why stereotype threat

718
00:30:23,110 --> 00:30:24,508
can lead to underperformance.

719
00:30:24,508 --> 00:30:26,800
Your brain's not doing what
your brain should be doing.

720
00:30:26,800 --> 00:30:30,970
It should be in the logic
area, not in the fear area.

721
00:30:30,970 --> 00:30:35,560
So this is one thing that's
talked about in his book.

722
00:30:35,560 --> 00:30:36,160
So anyway.

723
00:30:36,160 --> 00:30:38,560
So some of the
studies that he did,

724
00:30:38,560 --> 00:30:42,220
it was groups of
female undergrads.

725
00:30:42,220 --> 00:30:44,800
Stanford quite a good school.

726
00:30:44,800 --> 00:30:47,960
People who get in there,
very smart people.

727
00:30:47,960 --> 00:30:52,030
And so, he looked at the SAT
scores of a group of women,

728
00:30:52,030 --> 00:30:54,220
and then he divided
them into two groups.

729
00:30:54,220 --> 00:30:56,440
And they should have
equal math abilities

730
00:30:56,440 --> 00:30:58,820
based on their SAT scores.

731
00:30:58,820 --> 00:31:02,170
So for one group,
he either reminded

732
00:31:02,170 --> 00:31:04,510
them of their gender before
the exam, and in some case,

733
00:31:04,510 --> 00:31:05,890
didn't say anything at all.

734
00:31:05,890 --> 00:31:08,290
But in the second
group, he told them

735
00:31:08,290 --> 00:31:11,350
that the exam had
no gender bias.

736
00:31:11,350 --> 00:31:13,210
And so, he would
start out saying,

737
00:31:13,210 --> 00:31:16,000
well, you may have heard
that women often underperform

738
00:31:16,000 --> 00:31:19,390
on SATs in the math, but
we've been working here,

739
00:31:19,390 --> 00:31:21,550
and we've created
a math exam that

740
00:31:21,550 --> 00:31:25,900
has no gender bias,
so everyone can do up

741
00:31:25,900 --> 00:31:28,510
to their full ability.

742
00:31:28,510 --> 00:31:30,610
There's nothing
holding you back.

743
00:31:30,610 --> 00:31:33,610
And what was really interesting
that this group performed

744
00:31:33,610 --> 00:31:37,228
very well compared to the other
group that was either reminded

745
00:31:37,228 --> 00:31:38,770
of their gender, or
sometimes nothing

746
00:31:38,770 --> 00:31:40,330
was said about gender at all.

747
00:31:40,330 --> 00:31:42,700
So when these women
thought that they

748
00:31:42,700 --> 00:31:45,010
could do as well
as they could do,

749
00:31:45,010 --> 00:31:48,820
they over-performed or
outperformed the other group.

750
00:31:48,820 --> 00:31:50,290
So there's a number
of these kinds

751
00:31:50,290 --> 00:31:52,150
of studies done
in different ways

752
00:31:52,150 --> 00:31:55,540
showing again that
if you feel like you

753
00:31:55,540 --> 00:31:59,300
can reach your full
performance, you get there.

754
00:31:59,300 --> 00:32:03,790
But if you're reminded
of these issues,

755
00:32:03,790 --> 00:32:06,483
and that you might be confirming
a negative stereotype,

756
00:32:06,483 --> 00:32:07,900
or you're worried
about confirming

757
00:32:07,900 --> 00:32:11,490
a negative stereotype, it
can lead to poor performance.

758
00:32:11,490 --> 00:32:11,990
All right.

759
00:32:11,990 --> 00:32:16,370
So stereotype threat can
lead to underperformance,

760
00:32:16,370 --> 00:32:21,430
which is a problem in
a classroom, for sure.

761
00:32:21,430 --> 00:32:23,200
The other point that
I brought up before,

762
00:32:23,200 --> 00:32:26,110
and I want to come back
to, is that it can also

763
00:32:26,110 --> 00:32:30,130
lead to a feeling of
being judged unfairly.

764
00:32:30,130 --> 00:32:31,810
And this is one of
my favorite studies,

765
00:32:31,810 --> 00:32:35,300
and it's back from the 1980s.

766
00:32:35,300 --> 00:32:38,740
So in this study they
said, all right, we

767
00:32:38,740 --> 00:32:41,770
want to address the
differences-- how people

768
00:32:41,770 --> 00:32:46,570
are treated if they have some
kind of physical disfigurement,

769
00:32:46,570 --> 00:32:48,873
in particular, a
scar on your face.

770
00:32:48,873 --> 00:32:50,290
So he said, what
we're going to do

771
00:32:50,290 --> 00:32:53,230
is we're going to bring in a
makeup artist from Hollywood,

772
00:32:53,230 --> 00:32:56,410
they're going to put
a scar on your face,

773
00:32:56,410 --> 00:32:59,260
and then we're going to
send you into this room

774
00:32:59,260 --> 00:33:01,840
and we're going to
watch the interactions.

775
00:33:01,840 --> 00:33:04,210
And we want you to
tell us, are you

776
00:33:04,210 --> 00:33:05,890
being treated
differently because you

777
00:33:05,890 --> 00:33:07,810
have this scar on your face?

778
00:33:07,810 --> 00:33:10,390
So the volunteers sat
in front of the mirror.

779
00:33:10,390 --> 00:33:13,000
They had the makeup
artist come and this scar

780
00:33:13,000 --> 00:33:14,560
was put on their face.

781
00:33:14,560 --> 00:33:17,350
And then right before they
were supposed to go in,

782
00:33:17,350 --> 00:33:18,920
the makeup artist
came back and said,

783
00:33:18,920 --> 00:33:21,503
we're just going to touch it up,
make sure you're ready to go.

784
00:33:21,503 --> 00:33:25,720
They removed it entirely,
but the person had no idea.

785
00:33:25,720 --> 00:33:28,660
They thought that the scar
was still on the face.

786
00:33:28,660 --> 00:33:34,360
Went into the room, there
was a discussion, came out.

787
00:33:34,360 --> 00:33:38,520
The volunteer said, oh, I was
treated entirely differently

788
00:33:38,520 --> 00:33:40,582
with this scar on my face.

789
00:33:40,582 --> 00:33:41,790
And other people had watched.

790
00:33:41,790 --> 00:33:43,560
There was nothing going on.

791
00:33:43,560 --> 00:33:45,060
But they were just sensing--

792
00:33:45,060 --> 00:33:47,490
they were looking
for signs that they

793
00:33:47,490 --> 00:33:49,170
were being treated unfairly.

794
00:33:49,170 --> 00:33:51,420
And when you look for
things, sometimes you

795
00:33:51,420 --> 00:33:56,190
can find them or interpret them,
when they don't really exist.

796
00:33:56,190 --> 00:33:59,160
So right now, some
people are sitting,

797
00:33:59,160 --> 00:34:02,580
might have their arms
crossed a little bit.

798
00:34:02,580 --> 00:34:06,780
And if I were an insecure
person, I could say,

799
00:34:06,780 --> 00:34:09,060
that person's sitting
there is thinking

800
00:34:09,060 --> 00:34:13,199
there is nothing this woman has
to tell me that I want to hear.

801
00:34:13,199 --> 00:34:15,219
And they just are
sitting like that.

802
00:34:15,219 --> 00:34:18,600
But then I also know it's
a little cold in this room.

803
00:34:18,600 --> 00:34:20,520
Someone might just
be a little bit cold,

804
00:34:20,520 --> 00:34:22,800
and that's why they're
sitting like this, right?

805
00:34:22,800 --> 00:34:25,530
You can interpret
all sorts of things.

806
00:34:25,530 --> 00:34:29,489
If you're looking for a sign
that somebody is questioning

807
00:34:29,489 --> 00:34:33,977
you, you might be able to find
it even when it's not there.

808
00:34:33,977 --> 00:34:35,310
And sometimes it's really there.

809
00:34:35,310 --> 00:34:36,810
Sometimes people are judging.

810
00:34:36,810 --> 00:34:37,350
Oh, yeah.

811
00:34:37,350 --> 00:34:37,850
[LAUGHTER]

812
00:34:37,850 --> 00:34:39,830
We can have stories later.

813
00:34:39,830 --> 00:34:42,065
But sometimes they're not.

814
00:34:42,065 --> 00:34:43,690
So I just want to
give you one example.

815
00:34:43,690 --> 00:34:46,540
So this is the other counter--

816
00:34:46,540 --> 00:34:49,449
a guide to reducing
stereotype threat

817
00:34:49,449 --> 00:34:52,000
and maximizing student
performance, also

818
00:34:52,000 --> 00:34:55,780
a guide to Cathy's most
embarrassing moments.

819
00:34:55,780 --> 00:34:59,680
But there was one time I was
meeting with a professor,

820
00:34:59,680 --> 00:35:00,740
I was pre-tenure.

821
00:35:00,740 --> 00:35:02,920
Again, I wanted to get tenure.

822
00:35:02,920 --> 00:35:04,420
Very nervous about
this whole thing.

823
00:35:04,420 --> 00:35:06,753
And so, this person in my
field, likely a letter-writer,

824
00:35:06,753 --> 00:35:07,990
is coming to my office.

825
00:35:07,990 --> 00:35:09,220
And I was really excited.

826
00:35:09,220 --> 00:35:11,950
So I was like, I had results
that he was going to like.

827
00:35:11,950 --> 00:35:13,600
I knew he would
like these results.

828
00:35:13,600 --> 00:35:15,940
I printed him out,
like these slides.

829
00:35:15,940 --> 00:35:17,710
I had everything all ready.

830
00:35:17,710 --> 00:35:19,450
I thought through my spiel.

831
00:35:19,450 --> 00:35:20,220
I was psyched.

832
00:35:20,220 --> 00:35:21,490
He he came.

833
00:35:21,490 --> 00:35:22,990
And so he came
in, he's like, I'm

834
00:35:22,990 --> 00:35:24,460
so glad you're here visiting.

835
00:35:24,460 --> 00:35:25,043
This is great.

836
00:35:25,043 --> 00:35:26,877
I have these new results
I want to show you,

837
00:35:26,877 --> 00:35:27,730
and I pull this out.

838
00:35:27,730 --> 00:35:29,970
And he goes, you know,
I was sort of wondering,

839
00:35:29,970 --> 00:35:31,510
how's your experience been here?

840
00:35:31,510 --> 00:35:32,470
I was like, good, good.

841
00:35:32,470 --> 00:35:33,100
Very good.

842
00:35:33,100 --> 00:35:34,790
Now, we had this result--

843
00:35:34,790 --> 00:35:37,720
and he's like, but I
had some questions.

844
00:35:37,720 --> 00:35:41,190
Do you feel like
you've been treated OK?

845
00:35:41,190 --> 00:35:42,420
30 minutes went by.

846
00:35:42,420 --> 00:35:45,660
He did not look at a
single result that I had.

847
00:35:45,660 --> 00:35:47,910
And I just thought,
this guy has just

848
00:35:47,910 --> 00:35:52,500
decided that there is no
result that I could possibly

849
00:35:52,500 --> 00:35:56,310
have that would be interesting
enough to pay attention to.

850
00:35:56,310 --> 00:35:59,220
And so, he's just going to
spend this half hour chatting

851
00:35:59,220 --> 00:36:03,060
about this and that, and things
about me being a junior faculty

852
00:36:03,060 --> 00:36:09,590
member, whatever, and just
clearly disrespectful of me.

853
00:36:09,590 --> 00:36:11,140
So that was my take.

854
00:36:11,140 --> 00:36:14,710
Find out later he has a son
that's starting as an Assistant

855
00:36:14,710 --> 00:36:15,730
Professor.

856
00:36:15,730 --> 00:36:19,510
He wants to give his son advice,
but he hasn't been an Assistant

857
00:36:19,510 --> 00:36:20,770
Professor for a long time.

858
00:36:20,770 --> 00:36:22,060
Things have changed.

859
00:36:22,060 --> 00:36:24,935
So he said, when I go to
talk to Cathy Drennan,

860
00:36:24,935 --> 00:36:26,560
I'm going to ask her
a lot of questions

861
00:36:26,560 --> 00:36:28,270
about being an
Assistant Professor,

862
00:36:28,270 --> 00:36:31,810
because she has been so
hugely successful that I

863
00:36:31,810 --> 00:36:34,870
want to take how
she's done things

864
00:36:34,870 --> 00:36:37,240
and tell my son about it.

865
00:36:37,240 --> 00:36:41,680
So I could not have
been more wrong.

866
00:36:41,680 --> 00:36:44,230
I thought he was
judging me unfairly.

867
00:36:44,230 --> 00:36:47,840
Turns out, that was
not the case at all.

868
00:36:47,840 --> 00:36:49,780
And so, because other people--

869
00:36:49,780 --> 00:36:54,850
I've one male professor explain
to another male professor

870
00:36:54,850 --> 00:36:56,950
my data without involving
me in the discussion,

871
00:36:56,950 --> 00:36:58,810
because what could I
possibly know about it?

872
00:36:58,810 --> 00:37:01,390
So I've had these
experiences, but then I'm

873
00:37:01,390 --> 00:37:03,770
looking for these
experiences in other places,

874
00:37:03,770 --> 00:37:05,410
and sometimes they're
not there at all.

875
00:37:05,410 --> 00:37:07,600
Sometimes a person
is being respectful.

876
00:37:07,600 --> 00:37:11,260
So when you feel like you're
under stereotype threat,

877
00:37:11,260 --> 00:37:16,270
you can sometimes take a
situation entirely incorrectly.

878
00:37:16,270 --> 00:37:19,290
Now, he could have
also said, hey, Cathy,

879
00:37:19,290 --> 00:37:20,770
you've been so successful.

880
00:37:20,770 --> 00:37:22,420
Let me ask you questions.

881
00:37:22,420 --> 00:37:25,210
That could have-- but whatever.

882
00:37:25,210 --> 00:37:26,320
Whatever.

883
00:37:26,320 --> 00:37:27,970
So I think this
is something also

884
00:37:27,970 --> 00:37:31,620
to think about, that people
are looking to each other like,

885
00:37:31,620 --> 00:37:32,375
do you trust?

886
00:37:32,375 --> 00:37:33,250
Do you believe in me?

887
00:37:33,250 --> 00:37:35,020
Do you trust what
I'm going to say?

888
00:37:35,020 --> 00:37:37,210
We're trying to get
a sense of this.

889
00:37:37,210 --> 00:37:40,480
And sometimes we
may get it wrong,

890
00:37:40,480 --> 00:37:42,760
and sometimes it
may not be there,

891
00:37:42,760 --> 00:37:45,530
but everyone in the
classroom is not the same.

892
00:37:45,530 --> 00:37:47,030
Even if you treat
everyone the same,

893
00:37:47,030 --> 00:37:48,430
they're not always the
same, because people

894
00:37:48,430 --> 00:37:49,840
are coming in with baggage.

895
00:37:49,840 --> 00:37:52,510
And I've got baggage,
let me tell you.

896
00:37:52,510 --> 00:37:53,620
I got some baggage.

897
00:37:53,620 --> 00:37:56,350
I'll put my baggage up
against your baggage,

898
00:37:56,350 --> 00:37:59,130
but everyone is coming
in with something.

899
00:37:59,130 --> 00:37:59,820
All right.

900
00:37:59,820 --> 00:38:03,090
So I think you already know
the answer to this one.

901
00:38:03,090 --> 00:38:05,490
Who can be a victim
of stereotype threat?

902
00:38:05,490 --> 00:38:06,802
ENTIRE AUDIENCE: Everyone!

903
00:38:06,802 --> 00:38:08,135
CATHY L. DRENNAN: Yes, everyone.

904
00:38:08,135 --> 00:38:09,920
[LAUGHTER]

905
00:38:09,920 --> 00:38:13,280
In fact, studies show that it's
the strongest students that

906
00:38:13,280 --> 00:38:14,870
are the most affected.

907
00:38:14,870 --> 00:38:17,660
So if you care about
what people think of you,

908
00:38:17,660 --> 00:38:20,510
if you care about doing
well, if you're ambitious,

909
00:38:20,510 --> 00:38:24,440
if you want to do important
things in your life,

910
00:38:24,440 --> 00:38:26,970
you care about these
kinds of things.

911
00:38:26,970 --> 00:38:28,880
And if you're at
a place that has

912
00:38:28,880 --> 00:38:31,880
a good reputation,
like MIT, that

913
00:38:31,880 --> 00:38:33,830
can lead to imposter syndrome.

914
00:38:33,830 --> 00:38:36,620
So most you've probably
heard of imposter syndrome

915
00:38:36,620 --> 00:38:39,680
before, but this
idea that somehow

916
00:38:39,680 --> 00:38:42,230
you were only accepted to MIT--

917
00:38:42,230 --> 00:38:43,953
your name is like
someone else, and they

918
00:38:43,953 --> 00:38:45,120
weren't going to accept you.

919
00:38:45,120 --> 00:38:48,140
But then they made this mistake
and then somehow you got in,

920
00:38:48,140 --> 00:38:50,450
said the person who was
below you in the line when

921
00:38:50,450 --> 00:38:51,860
they were supposed to get in.

922
00:38:51,860 --> 00:38:54,710
And that everyone else here
is brilliant except for you,

923
00:38:54,710 --> 00:38:56,500
and someday that
people are going

924
00:38:56,500 --> 00:39:00,320
to figure out that you're
trying to look like a nerd--

925
00:39:00,320 --> 00:39:01,340
or a horse.

926
00:39:01,340 --> 00:39:04,400
You know, you've got
your horse stockings on

927
00:39:04,400 --> 00:39:06,260
and your horse cape.

928
00:39:06,260 --> 00:39:08,270
Or your zebra cape
and your zebra

929
00:39:08,270 --> 00:39:10,650
stockings, but you're
not really a zebra.

930
00:39:10,650 --> 00:39:14,240
And that you're wearing
your nerdiest clothes,

931
00:39:14,240 --> 00:39:17,210
but you're really
not an MIT nerd.

932
00:39:17,210 --> 00:39:21,770
So this is something that so
many people at top universities

933
00:39:21,770 --> 00:39:25,400
are affected by, and
this is really important

934
00:39:25,400 --> 00:39:28,280
when people are, again,
under stereotype threat.

935
00:39:28,280 --> 00:39:30,920
They're feeling like, man, I'm
the only one who doesn't really

936
00:39:30,920 --> 00:39:32,480
belong here.

937
00:39:32,480 --> 00:39:34,990
So imposter syndrome.

938
00:39:34,990 --> 00:39:35,490
OK.

939
00:39:35,490 --> 00:39:38,900
Another teaching tip, because
you'll all be teaching.

940
00:39:38,900 --> 00:39:41,360
Before you teach on
something, look up

941
00:39:41,360 --> 00:39:42,600
what it says on Wikipedia.

942
00:39:42,600 --> 00:39:45,890
Just double check what it says.

943
00:39:45,890 --> 00:39:49,220
So what do you
think Wikipedia says

944
00:39:49,220 --> 00:39:53,360
of the people who are most
affected by imposter syndrome?

945
00:39:53,360 --> 00:39:55,490
What particular group?

946
00:39:55,490 --> 00:39:56,090
Any guesses?

947
00:40:00,790 --> 00:40:01,290
Men?

948
00:40:01,290 --> 00:40:02,207
AUDIENCE: [INAUDIBLE].

949
00:40:02,207 --> 00:40:03,990
[LAUGHTER]

950
00:40:03,990 --> 00:40:05,090
Graduate students.

951
00:40:05,090 --> 00:40:07,750
Someone has been
googling Wikipedia.

952
00:40:07,750 --> 00:40:11,000
That is, in fact, correct.

953
00:40:11,000 --> 00:40:15,590
So a lot of people
say, oh, it's women.

954
00:40:15,590 --> 00:40:20,740
But the data actually shows that
men and women suffer equally,

955
00:40:20,740 --> 00:40:22,280
which is not really
my experience.

956
00:40:22,280 --> 00:40:25,680
I think that if this is true,
it's just that women talk about

957
00:40:25,680 --> 00:40:28,050
it all the time and men don't.

958
00:40:28,050 --> 00:40:30,240
But apparently, the
data shows that it's

959
00:40:30,240 --> 00:40:33,060
equal among men and women.

960
00:40:33,060 --> 00:40:36,060
And it is commonly
associated with academics

961
00:40:36,060 --> 00:40:39,720
and widely found among
graduate students.

962
00:40:39,720 --> 00:40:42,120
So again, it's
people who want to do

963
00:40:42,120 --> 00:40:44,400
important things with her life.

964
00:40:44,400 --> 00:40:45,300
Graduate school.

965
00:40:45,300 --> 00:40:47,340
Anyone who's in
graduate school--

966
00:40:47,340 --> 00:40:49,800
that's a pretty big deal that
you're in graduate school.

967
00:40:49,800 --> 00:40:52,853
So you really you care
about what people think.

968
00:40:52,853 --> 00:40:54,270
You care about
whether you belong.

969
00:40:54,270 --> 00:40:56,850
You're thinking, how did I
end up in graduate school,

970
00:40:56,850 --> 00:41:00,640
how did I end up at MIT,
and do I really belong here?

971
00:41:00,640 --> 00:41:04,380
So these are natural
things for people to think.

972
00:41:04,380 --> 00:41:07,470
And I will tell you
that a number of faculty

973
00:41:07,470 --> 00:41:10,990
have reported that yes they
suffer from imposter syndrome.

974
00:41:10,990 --> 00:41:15,180
I think anyone who is honest
will say that they do.

975
00:41:15,180 --> 00:41:17,570
So this is a big problem.

976
00:41:17,570 --> 00:41:18,080
All right.

977
00:41:18,080 --> 00:41:21,760
So let's do another exercise.

978
00:41:21,760 --> 00:41:24,200
You might have wondered about
the title of Claude Steele's

979
00:41:24,200 --> 00:41:27,110
book that I mentioned,
Whistling Vivaldi.

980
00:41:27,110 --> 00:41:29,420
Where does that title come from?

981
00:41:29,420 --> 00:41:31,460
So he explains the title,
which is a good thing,

982
00:41:31,460 --> 00:41:35,120
because it is an unusual title.

983
00:41:35,120 --> 00:41:38,390
So he said that it
comes from a story

984
00:41:38,390 --> 00:41:42,170
that he heard of this young
African-American man who

985
00:41:42,170 --> 00:41:44,370
was in the South
Side of Chicago,

986
00:41:44,370 --> 00:41:46,425
and this man was a musician.

987
00:41:46,425 --> 00:41:48,050
And he was walking
one night, and there

988
00:41:48,050 --> 00:41:52,610
was an elderly couple walking
on the street right near him.

989
00:41:52,610 --> 00:41:56,570
And they saw him
walking near them,

990
00:41:56,570 --> 00:41:59,623
and he saw them walking faster.

991
00:41:59,623 --> 00:42:01,790
And they were an elderly
couple, so they were really

992
00:42:01,790 --> 00:42:04,070
trying to walk fast.

993
00:42:04,070 --> 00:42:08,060
And he felt kind of bad
because they were kind of old.

994
00:42:08,060 --> 00:42:10,430
And so, he wanted to
kind of yell out at them,

995
00:42:10,430 --> 00:42:12,350
you do not need
to worry about me.

996
00:42:12,350 --> 00:42:14,990
I'm not going to do
anything bad to you.

997
00:42:14,990 --> 00:42:20,000
But yelling that probably
would be even more terrifying.

998
00:42:20,000 --> 00:42:22,540
So we thought about
it and he said,

999
00:42:22,540 --> 00:42:24,550
I'm going to start
whistling Vivaldi,

1000
00:42:24,550 --> 00:42:28,000
actually whistling
the music of Vivaldi.

1001
00:42:28,000 --> 00:42:30,890
And so, he started whistling
the music and the couple

1002
00:42:30,890 --> 00:42:33,280
slowed their pace,
because they thought,

1003
00:42:33,280 --> 00:42:35,740
this is someone who
is classically trained

1004
00:42:35,740 --> 00:42:38,620
or loves classical music.

1005
00:42:38,620 --> 00:42:40,510
Not so much associated
with someone who's

1006
00:42:40,510 --> 00:42:42,430
going to steal my handbag.

1007
00:42:42,430 --> 00:42:44,140
And so, they relaxed.

1008
00:42:44,140 --> 00:42:49,150
So in this book, Steele
asserts that we all whistle

1009
00:42:49,150 --> 00:42:50,720
Vivaldi from time to time.

1010
00:42:50,720 --> 00:42:53,530
We all try to fit in and
make other people more

1011
00:42:53,530 --> 00:42:55,480
comfortable with us.

1012
00:42:55,480 --> 00:42:59,350
So this is a little bit of a
harder thing to think about.

1013
00:42:59,350 --> 00:43:01,840
Have you ever whistled Vivaldi?

1014
00:43:01,840 --> 00:43:04,590
Have you ever tried to fit in?

1015
00:43:04,590 --> 00:43:06,722
And let me just give you
an example that I heard

1016
00:43:06,722 --> 00:43:07,930
to help you think about this.

1017
00:43:07,930 --> 00:43:11,230
So one student said
that he was actually

1018
00:43:11,230 --> 00:43:14,140
from a very religious
family, and he

1019
00:43:14,140 --> 00:43:17,680
was used to praying
before eating a meal.

1020
00:43:17,680 --> 00:43:22,030
But in the lunch room of
a science building somehow

1021
00:43:22,030 --> 00:43:25,350
that felt weird to
pray before eating.

1022
00:43:25,350 --> 00:43:28,840
A lot of people have
this idea that if you're

1023
00:43:28,840 --> 00:43:32,170
very religious
that somehow that's

1024
00:43:32,170 --> 00:43:35,200
counter to being a
scientist, because you have

1025
00:43:35,200 --> 00:43:39,850
faith vs. Scientific facts.

1026
00:43:39,850 --> 00:43:43,960
And he wasn't sure how people
would judge him if they

1027
00:43:43,960 --> 00:43:46,060
knew about his religion.

1028
00:43:46,060 --> 00:43:48,490
So even though he
really did want to pray,

1029
00:43:48,490 --> 00:43:50,110
he stopped doing
it because he just

1030
00:43:50,110 --> 00:43:51,733
thought he wouldn't fit in.

1031
00:43:51,733 --> 00:43:53,650
It would make other
people feel uncomfortable.

1032
00:43:53,650 --> 00:43:55,600
He wasn't sure how
others would feel,

1033
00:43:55,600 --> 00:43:58,720
and if they would accept him
the same way as a scientist

1034
00:43:58,720 --> 00:44:00,590
if he prayed before a meal.

1035
00:44:00,590 --> 00:44:02,620
So that's an example
of whistling Vivaldi.

1036
00:44:02,620 --> 00:44:03,970
So think about it for a minute.

1037
00:44:03,970 --> 00:44:05,525
Find your partner.

1038
00:44:05,525 --> 00:44:07,900
Talk to them about whether
this has ever happened to you,

1039
00:44:07,900 --> 00:44:09,400
and then we'll
come back together

1040
00:44:09,400 --> 00:44:10,630
as a group in a few minutes.

1041
00:44:15,380 --> 00:44:19,210
AUDIENCE: Yeah, so when I
think about whistling Vivaldi,

1042
00:44:19,210 --> 00:44:22,280
for me, the point where
I feel like I'm really

1043
00:44:22,280 --> 00:44:24,850
changing or trying to
conform to an appearance

1044
00:44:24,850 --> 00:44:28,640
is pretty much any
time after 9/11.

1045
00:44:28,640 --> 00:44:31,040
When I go through
the airports I'm

1046
00:44:31,040 --> 00:44:35,660
always conscious of what
people might be thinking about

1047
00:44:35,660 --> 00:44:38,600
based on my skin color.

1048
00:44:38,600 --> 00:44:45,230
And so, when I go through,
every day right before I travel,

1049
00:44:45,230 --> 00:44:46,850
I shave my beard off.

1050
00:44:46,850 --> 00:44:51,560
I try to be dressed
up a little nicer.

1051
00:44:51,560 --> 00:44:53,780
And I even feel myself being
a little extra friendly

1052
00:44:53,780 --> 00:44:57,530
to TSA gate agents,
because I feel

1053
00:44:57,530 --> 00:45:00,290
like there's this negative
stereotype hanging over me.

1054
00:45:00,290 --> 00:45:06,470
And I feel like even
though it's on my mind,

1055
00:45:06,470 --> 00:45:09,020
and it feels sometimes like it
might be on everybody else's.

1056
00:45:09,020 --> 00:45:12,765
I feel like maybe my ID's
looked at a little differently,

1057
00:45:12,765 --> 00:45:13,640
and things like that.

1058
00:45:13,640 --> 00:45:16,990
So I make sure they can
hear my American accent,

1059
00:45:16,990 --> 00:45:20,352
make sure they don't
see any facial hair.

1060
00:45:20,352 --> 00:45:21,060
Things like that.

1061
00:45:21,060 --> 00:45:24,920
I try to be extra
nice, extra polite.

1062
00:45:24,920 --> 00:45:26,830
Just feeling a little
more nervous, I think,

1063
00:45:26,830 --> 00:45:29,698
is really the cause
of that as well.

1064
00:45:29,698 --> 00:45:31,490
AUDIENCE: Yeah, that's
really good example.

1065
00:45:31,490 --> 00:45:32,198
AUDIENCE: Mm-hmm.

1066
00:45:32,198 --> 00:45:34,620
AUDIENCE: Mine would be
in professional settings,

1067
00:45:34,620 --> 00:45:39,080
especially at conferences or
any presentation of some sort,

1068
00:45:39,080 --> 00:45:42,740
I try to cover my tattoos
the best way I can.

1069
00:45:42,740 --> 00:45:45,020
And I also have a nose
ring, and so sometimes I

1070
00:45:45,020 --> 00:45:47,400
would wear like
a clear nose ring

1071
00:45:47,400 --> 00:45:49,880
so people only if they're
really up close to me

1072
00:45:49,880 --> 00:45:50,960
can see that I have one.

1073
00:45:50,960 --> 00:45:54,050
And that's just to mitigate
any stereotype threats

1074
00:45:54,050 --> 00:45:55,610
that people have.

1075
00:45:55,610 --> 00:45:58,550
Because you walk
into a room and you

1076
00:45:58,550 --> 00:46:02,000
have tattoos visible,
or your piercings,

1077
00:46:02,000 --> 00:46:03,410
especially on your
face, and that

1078
00:46:03,410 --> 00:46:06,410
can lead people to think
differently about you

1079
00:46:06,410 --> 00:46:07,800
and what you're
capable of doing.

1080
00:46:07,800 --> 00:46:09,217
And so, I just
want to lessen that

1081
00:46:09,217 --> 00:46:12,815
and have people look past that.

1082
00:46:12,815 --> 00:46:13,440
AUDIENCE: Yeah.

1083
00:46:13,440 --> 00:46:13,690
AUDIENCE: Yeah.

1084
00:46:13,690 --> 00:46:15,240
AUDIENCE: I think that's
a really good example.

1085
00:46:15,240 --> 00:46:16,823
I mean, it's all the
same thing, is we

1086
00:46:16,823 --> 00:46:20,267
feel like our appearances
are telling something,

1087
00:46:20,267 --> 00:46:22,100
and we're really worried
that the message is

1088
00:46:22,100 --> 00:46:23,305
going to be negative there.

1089
00:46:26,438 --> 00:46:26,980
AUDIENCE: OK.

1090
00:46:26,980 --> 00:46:30,590
So when have we
whistled Vivaldi?

1091
00:46:30,590 --> 00:46:31,660
AUDIENCE: Yeah.

1092
00:46:31,660 --> 00:46:35,140
I think I especially
do it whenever

1093
00:46:35,140 --> 00:46:36,730
I'm with friends and family.

1094
00:46:36,730 --> 00:46:43,430
So since English isn't
my native language,

1095
00:46:43,430 --> 00:46:45,790
but I feel like I can
speak it pretty well,

1096
00:46:45,790 --> 00:46:49,930
so then whenever I go back
home, my friends and family

1097
00:46:49,930 --> 00:46:53,710
don't always speak
English, I actually

1098
00:46:53,710 --> 00:46:55,690
intentionally mispronounce
certain words just

1099
00:46:55,690 --> 00:46:58,420
to keep that
connection with them.

1100
00:46:58,420 --> 00:46:59,490
AUDIENCE: Yeah.

1101
00:46:59,490 --> 00:47:03,480
AUDIENCE: Since I've been
gone for like a few months,

1102
00:47:03,480 --> 00:47:07,420
and seeing me every
so often, I just

1103
00:47:07,420 --> 00:47:10,485
want them to feel like I'm
still the same me I was.

1104
00:47:10,485 --> 00:47:11,110
AUDIENCE: Yeah.

1105
00:47:11,110 --> 00:47:12,485
AUDIENCE: And then
when I'm here,

1106
00:47:12,485 --> 00:47:16,430
I always try to pronounce
everything super well,

1107
00:47:16,430 --> 00:47:19,851
so that's not an issue when
someone's talking to me.

1108
00:47:19,851 --> 00:47:20,476
AUDIENCE: Yeah.

1109
00:47:20,476 --> 00:47:21,250
AUDIENCE: Yeah.

1110
00:47:21,250 --> 00:47:23,940
AUDIENCE: [INAUDIBLE] similar?

1111
00:47:23,940 --> 00:47:26,920
AUDIENCE: Yeah,
so I guess for me,

1112
00:47:26,920 --> 00:47:29,900
one of the big things
about grad school

1113
00:47:29,900 --> 00:47:35,350
was that I sometimes felt
pretty isolated as one of very

1114
00:47:35,350 --> 00:47:37,080
few women in our department.

1115
00:47:37,080 --> 00:47:42,280
And so, over time, I realized I
started dressing more and more

1116
00:47:42,280 --> 00:47:45,760
like a man, to fit
in and to be taken

1117
00:47:45,760 --> 00:47:48,190
more seriously, and
just so that I didn't

1118
00:47:48,190 --> 00:47:49,660
have to feel as different.

1119
00:47:49,660 --> 00:47:51,100
AUDIENCE: Yeah.

1120
00:47:51,100 --> 00:47:55,930
So, yes, like with that,
I feel like I've always

1121
00:47:55,930 --> 00:48:01,280
wanted to be who I am, and I
don't want to be somebody else.

1122
00:48:01,280 --> 00:48:03,620
So I guess, along
those lines, I've

1123
00:48:03,620 --> 00:48:08,210
had one conversation
with one of my professors

1124
00:48:08,210 --> 00:48:12,940
where she was explaining that
in research, when people were

1125
00:48:12,940 --> 00:48:14,970
giving presentations,
they usually

1126
00:48:14,970 --> 00:48:19,320
wore light colors like
gray, and white, or black,

1127
00:48:19,320 --> 00:48:22,180
and that you shouldn't
be too colorful.

1128
00:48:22,180 --> 00:48:25,180
One time we were going
into a conference,

1129
00:48:25,180 --> 00:48:27,130
and I was wearing red.

1130
00:48:27,130 --> 00:48:31,360
I was like really colorful,
and then she was like, wow.

1131
00:48:31,360 --> 00:48:33,250
I mean, that's not
what we talked about.

1132
00:48:33,250 --> 00:48:34,090
I'm like, yeah.

1133
00:48:34,090 --> 00:48:35,710
Science can be colorful too.

1134
00:48:35,710 --> 00:48:38,770
AUDIENCE: That's great. (LAUGHS)

1135
00:48:38,770 --> 00:48:40,750
AUDIENCE: I want to be who I am.

1136
00:48:40,750 --> 00:48:45,400
And the next minute, I saw a
professor, he was so colorful,

1137
00:48:45,400 --> 00:48:47,610
like red and orange
all over the place.

1138
00:48:47,610 --> 00:48:48,340
I was like, yes.

1139
00:48:48,340 --> 00:48:49,600
That's what I'm talking about.

1140
00:48:49,600 --> 00:48:50,380
Be who you are.

1141
00:48:50,380 --> 00:48:51,680
Be free.

1142
00:48:51,680 --> 00:48:53,320
And that's how a
science should be.

1143
00:48:53,320 --> 00:48:57,133
People should be comfortable
in their skin and who they are.

1144
00:48:57,133 --> 00:48:57,758
AUDIENCE: Yeah.

1145
00:48:57,758 --> 00:48:58,383
AUDIENCE: Yeah.

1146
00:48:58,383 --> 00:48:59,590
AUDIENCE: Thank you.

1147
00:48:59,590 --> 00:49:03,220
AUDIENCE: When I'm meeting
new scientists, especially

1148
00:49:03,220 --> 00:49:09,640
younger ones, or people who I
might have to mentor or train,

1149
00:49:09,640 --> 00:49:11,920
if they ever start to open
up about an experience

1150
00:49:11,920 --> 00:49:15,220
where they felt discriminated
against or harassed,

1151
00:49:15,220 --> 00:49:17,350
I can't necessarily
relate to that experience,

1152
00:49:17,350 --> 00:49:19,660
but I do want to convey
that they can trust me,

1153
00:49:19,660 --> 00:49:21,610
and that I'll listen
to them, and I'll

1154
00:49:21,610 --> 00:49:23,955
help them in whatever way.

1155
00:49:23,955 --> 00:49:26,080
Because I can't really
share a personal experience.

1156
00:49:26,080 --> 00:49:30,580
A lot of times I'll basically
turned to relevant literature,

1157
00:49:30,580 --> 00:49:32,020
like the book Whistling Vivaldi.

1158
00:49:32,020 --> 00:49:35,710
And I'll try to communicate,
I'm on your side and I'm safe,

1159
00:49:35,710 --> 00:49:39,690
by talking about a subject
that communicates to them

1160
00:49:39,690 --> 00:49:41,357
I'm putting effort
into this and I care.

1161
00:49:41,357 --> 00:49:42,982
AUDIENCE: Oh, that's
a really good one.

1162
00:49:42,982 --> 00:49:44,450
It makes a lot of sense for me.

1163
00:49:44,450 --> 00:49:47,740
It's more just going back
again to my identities.

1164
00:49:47,740 --> 00:49:50,135
I have an accent, because
I'm from the Caribbean--

1165
00:49:50,135 --> 00:49:51,760
and I feel like this
is common for lots

1166
00:49:51,760 --> 00:49:54,760
of international students
in the scientific setting--

1167
00:49:54,760 --> 00:49:57,850
I definitely try to put
on a more, quote, unquote,

1168
00:49:57,850 --> 00:50:01,720
American accent, to ensure
that my audience is not

1169
00:50:01,720 --> 00:50:05,080
being distracted by
my accent, but really

1170
00:50:05,080 --> 00:50:07,390
focusing on the science,
and really trying

1171
00:50:07,390 --> 00:50:10,280
to be more scientific,
quote, unquote.

1172
00:50:10,280 --> 00:50:12,490
And the other thing, in
terms of physical appearance,

1173
00:50:12,490 --> 00:50:13,740
I have a lot of hair.

1174
00:50:13,740 --> 00:50:15,670
[LAUGHTER]

1175
00:50:15,670 --> 00:50:19,450
It's very contained right
now, but it's really big.

1176
00:50:19,450 --> 00:50:22,630
So definitely, in
scientific settings,

1177
00:50:22,630 --> 00:50:24,820
I definitely try to pull
it back to look more

1178
00:50:24,820 --> 00:50:28,360
what, quote, unquote,
would be [INAUDIBLE] kept.

1179
00:50:28,360 --> 00:50:30,850
It's really kind
of weird, but yeah,

1180
00:50:30,850 --> 00:50:33,250
so you'll definitely see me
either my hair slicked back

1181
00:50:33,250 --> 00:50:36,070
in a scientific setting, versus
when you probably will see me

1182
00:50:36,070 --> 00:50:37,420
in lab, or something like that.

1183
00:50:37,420 --> 00:50:39,532
So that's definitely the
way I whistle Vivaldi,

1184
00:50:39,532 --> 00:50:40,990
because, again, I
don't want people

1185
00:50:40,990 --> 00:50:43,180
to be distracted by
my hair, or thinking

1186
00:50:43,180 --> 00:50:44,700
I don't look professional.

1187
00:50:44,700 --> 00:50:46,950
I don't want that to
detract from what I'm saying

1188
00:50:46,950 --> 00:50:49,010
and the science that I'm doing.

1189
00:50:49,010 --> 00:50:50,830
So that's definitely
another occasion

1190
00:50:50,830 --> 00:50:53,240
when I whistle Vivaldi.

1191
00:50:53,240 --> 00:50:56,700
AUDIENCE: Yeah, that
makes a lot of sense.

1192
00:50:56,700 --> 00:50:59,140
AUDIENCE: So I guess for
the whistling Vivaldi,

1193
00:50:59,140 --> 00:51:03,465
I did have an example
that reminded me of this

1194
00:51:03,465 --> 00:51:04,465
when I was an undergrad.

1195
00:51:04,465 --> 00:51:06,190
AUDIENCE: Mm-hmm.

1196
00:51:06,190 --> 00:51:09,340
AUDIENCE: So I joined
a lab my freshman year,

1197
00:51:09,340 --> 00:51:15,500
and I have always been more
traditionally feminine.

1198
00:51:15,500 --> 00:51:19,600
My, I don't know,
style dressing.

1199
00:51:19,600 --> 00:51:24,160
And I kind of noticed I was one
of the only women on the floor,

1200
00:51:24,160 --> 00:51:26,160
and I knew I stood out.

1201
00:51:26,160 --> 00:51:28,080
And I specifically
remember that there

1202
00:51:28,080 --> 00:51:32,307
was a professor who asked
me what I was doing around

1203
00:51:32,307 --> 00:51:34,890
on the lab floor, and told me I
didn't like the research type.

1204
00:51:34,890 --> 00:51:39,720
And that really stuck with
me, and after that, I really

1205
00:51:39,720 --> 00:51:40,510
changed.

1206
00:51:40,510 --> 00:51:45,480
I had my lab style and then
my everyday Lindsay style.

1207
00:51:45,480 --> 00:51:46,320
AUDIENCE: (LAUGHS)

1208
00:51:46,320 --> 00:51:47,820
AUDIENCE: Whenever
I was in the lab,

1209
00:51:47,820 --> 00:51:52,260
I would always wear very
baggy clothes, tennis shoes,

1210
00:51:52,260 --> 00:51:53,670
my hair tied back.

1211
00:51:53,670 --> 00:51:57,572
And I felt like my glasses made
me look more studious, so I

1212
00:51:57,572 --> 00:51:58,780
would always wear my glasses.

1213
00:52:01,500 --> 00:52:03,390
It's just something I
was very conscious of.

1214
00:52:03,390 --> 00:52:04,015
AUDIENCE: Yeah.

1215
00:52:04,015 --> 00:52:07,112
AUDIENCE: I would specifically
dressed in one way

1216
00:52:07,112 --> 00:52:09,570
when I was being a scientist,
and I would dress another way

1217
00:52:09,570 --> 00:52:11,652
when I was in my outside life.

1218
00:52:11,652 --> 00:52:13,860
AUDIENCE: It's like not
easy, because in some setting

1219
00:52:13,860 --> 00:52:15,468
you're judged for
being dressed up.

1220
00:52:15,468 --> 00:52:17,760
In other settings you're
judged for being dressed down.

1221
00:52:17,760 --> 00:52:18,385
AUDIENCE: Yeah.

1222
00:52:18,385 --> 00:52:19,970
AUDIENCE: Yeah, I
totally get that.

1223
00:52:19,970 --> 00:52:24,060
Yeah so, in my case, I think
it's more about my race.

1224
00:52:24,060 --> 00:52:27,210
I personally have a stereotype
that Asian women that

1225
00:52:27,210 --> 00:52:31,120
are recently immigrated
are more reserved, quiet,

1226
00:52:31,120 --> 00:52:33,180
and, I don't know, obedient.

1227
00:52:33,180 --> 00:52:37,230
And although no one actually
ever negatively judged

1228
00:52:37,230 --> 00:52:41,640
me for that, I'm constantly
afraid to confirm

1229
00:52:41,640 --> 00:52:43,200
that negative stereotype.

1230
00:52:43,200 --> 00:52:46,830
So even if I'm not really
that kind of person,

1231
00:52:46,830 --> 00:52:49,980
I try to be very vocal and
talkative, especially when I'm

1232
00:52:49,980 --> 00:52:52,620
surrounded by people that I'm
meeting for the first time,

1233
00:52:52,620 --> 00:52:54,900
so I'm giving like an
impression that I want

1234
00:52:54,900 --> 00:52:57,030
to engage in the conversation.

1235
00:52:57,030 --> 00:53:02,960
Yeah, it was not an easy
thing, but I've been doing it.

1236
00:53:02,960 --> 00:53:03,633
AUDIENCE: Yeah.

1237
00:53:03,633 --> 00:53:04,800
CATHY L. DRENNAN: All right.

1238
00:53:04,800 --> 00:53:06,653
So I'm just going to start out.

1239
00:53:06,653 --> 00:53:08,070
I'm going to give
you one example.

1240
00:53:08,070 --> 00:53:09,870
This time it's not
mine, but I think

1241
00:53:09,870 --> 00:53:14,070
that it was one of the
ones that someone suggested

1242
00:53:14,070 --> 00:53:16,000
that really resonated with me.

1243
00:53:16,000 --> 00:53:17,050
So I want to share it.

1244
00:53:17,050 --> 00:53:22,940
So this sense of
feeling the need

1245
00:53:22,940 --> 00:53:28,190
to suffer for your science,
and that other people

1246
00:53:28,190 --> 00:53:30,320
see someone that are
talking about how much they

1247
00:53:30,320 --> 00:53:31,490
suffered for their science.

1248
00:53:31,490 --> 00:53:35,730
And instead of just
saying, wait a minute,

1249
00:53:35,730 --> 00:53:37,620
I sleep eight hours a night.

1250
00:53:37,620 --> 00:53:39,780
They're like, yeah I gotta
get in on this suffering

1251
00:53:39,780 --> 00:53:41,040
for my science thing.

1252
00:53:41,040 --> 00:53:43,440
So an example,
someone was talking

1253
00:53:43,440 --> 00:53:46,560
about how they knew that
this person was going

1254
00:53:46,560 --> 00:53:49,410
on this ski trip
over the weekend,

1255
00:53:49,410 --> 00:53:52,440
but before they left lab to
go on the ski trip, instead

1256
00:53:52,440 --> 00:53:54,780
of saying, hey, see all Monday.

1257
00:53:54,780 --> 00:53:55,980
I'm going skiing.

1258
00:53:55,980 --> 00:53:59,400
They're going out like carrying
these journals and papers.

1259
00:53:59,400 --> 00:54:01,440
And they're just
like, oh, I got a lot

1260
00:54:01,440 --> 00:54:04,010
of reading to do this weekend.

1261
00:54:04,010 --> 00:54:05,190
Does anyone have a cart?

1262
00:54:05,190 --> 00:54:07,290
I can barely carry it.

1263
00:54:07,290 --> 00:54:11,760
I'm going to be
reading all weekend.

1264
00:54:11,760 --> 00:54:13,800
This is a scheme.

1265
00:54:13,800 --> 00:54:14,790
What's going on?

1266
00:54:14,790 --> 00:54:18,360
But it's like, if you're
really a scientist,

1267
00:54:18,360 --> 00:54:21,120
if you're a true scientist,
if you're a real scientist,

1268
00:54:21,120 --> 00:54:22,500
you suffer.

1269
00:54:22,500 --> 00:54:24,450
You have no hobbies.

1270
00:54:24,450 --> 00:54:28,350
You work day and night in
the cold room, in the dark.

1271
00:54:28,350 --> 00:54:33,240
I mean, you're just
36-hour experiments.

1272
00:54:33,240 --> 00:54:34,750
There's no time to shower.

1273
00:54:34,750 --> 00:54:36,660
There's no time to sleep.

1274
00:54:36,660 --> 00:54:39,900
And instead of just saying
and sending a message

1275
00:54:39,900 --> 00:54:41,940
to maybe new people in
lab, and undergrads,

1276
00:54:41,940 --> 00:54:44,970
and I think this has come up,
especially with women, that

1277
00:54:44,970 --> 00:54:48,690
like if you do not give
of yourself completely,

1278
00:54:48,690 --> 00:54:50,940
you're not a, quote,
real scientist.

1279
00:54:50,940 --> 00:54:54,000
And a lot of people who
are thinking about science,

1280
00:54:54,000 --> 00:54:57,330
especially women, think, I'd
really like to have a family,

1281
00:54:57,330 --> 00:54:59,830
and I know I might be doing a
little more of the caregiving.

1282
00:54:59,830 --> 00:55:01,440
So I don't know if
I can do science.

1283
00:55:01,440 --> 00:55:02,857
I might have to
do something else.

1284
00:55:02,857 --> 00:55:05,220
Because there's
this notion that you

1285
00:55:05,220 --> 00:55:08,370
have to suffer so
deeply for your science.

1286
00:55:08,370 --> 00:55:09,850
And people buy into this.

1287
00:55:09,850 --> 00:55:11,670
They don't want to
be the one saying,

1288
00:55:11,670 --> 00:55:13,830
I don't know what's wrong with
you, but I shower every day.

1289
00:55:13,830 --> 00:55:14,330
Right?

1290
00:55:14,330 --> 00:55:14,850
[LAUGHTER]

1291
00:55:14,850 --> 00:55:16,877
They're just like coming
up with something else

1292
00:55:16,877 --> 00:55:17,710
that they're saying.

1293
00:55:17,710 --> 00:55:21,270
It's like, well, yeah, but
my experiment was even worse!

1294
00:55:21,270 --> 00:55:24,420
And I had to walk uphill
both ways to do it, right?

1295
00:55:24,420 --> 00:55:27,420
Everyone gets into this
in the whole lab culture,

1296
00:55:27,420 --> 00:55:29,610
and they're trying
to outdo themselves

1297
00:55:29,610 --> 00:55:31,920
with the degree of suffering.

1298
00:55:31,920 --> 00:55:33,730
So that's one
example that came up.

1299
00:55:33,730 --> 00:55:36,570
And I do think that these kind
of things-- it sounds funny,

1300
00:55:36,570 --> 00:55:38,220
but it's important,
because it sends

1301
00:55:38,220 --> 00:55:42,460
a message of what it takes
to be a scientist that is not

1302
00:55:42,460 --> 00:55:43,700
really true.

1303
00:55:43,700 --> 00:55:47,170
And if we have well-adjusted
human beings doing science,

1304
00:55:47,170 --> 00:55:48,250
I'm OK for that.

1305
00:55:48,250 --> 00:55:51,340
I think that's a good thing to
have well-adjusted human beings

1306
00:55:51,340 --> 00:55:52,240
doing science.

1307
00:55:52,240 --> 00:55:57,400
So we have to think a little bit
about fitting into a culture,

1308
00:55:57,400 --> 00:56:00,360
and what that is, and
what we do to fit in.

1309
00:56:00,360 --> 00:56:00,860
All right.

1310
00:56:00,860 --> 00:56:03,880
So that's just one example
that was shared with me

1311
00:56:03,880 --> 00:56:05,930
before, which I
thought was a good one.

1312
00:56:05,930 --> 00:56:08,950
But it seems like you guys were
having some lively discussion.

1313
00:56:08,950 --> 00:56:11,980
So who would like to
start off by sharing

1314
00:56:11,980 --> 00:56:14,820
what you were talking about?

1315
00:56:14,820 --> 00:56:15,320
Yeah?

1316
00:56:15,320 --> 00:56:18,790
AUDIENCE: Yeah, so depending
on who I'm talking with,

1317
00:56:18,790 --> 00:56:21,340
I actually tune my accent.

1318
00:56:21,340 --> 00:56:23,680
For example, when I'm
back home, I intentionally

1319
00:56:23,680 --> 00:56:27,580
mispronounce certain words so
that I can talk with my friends

1320
00:56:27,580 --> 00:56:28,930
and family.

1321
00:56:28,930 --> 00:56:31,510
And then whenever I'm here
in an academic setting,

1322
00:56:31,510 --> 00:56:34,630
I try to pronounce
everything correctly.

1323
00:56:34,630 --> 00:56:35,420
Yeah.

1324
00:56:35,420 --> 00:56:37,630
CATHY L. DRENNAN: Yeah,
so the sort of, I'm

1325
00:56:37,630 --> 00:56:38,950
still sort of one of you.

1326
00:56:38,950 --> 00:56:42,580
I've gone off to
this place of MIT,

1327
00:56:42,580 --> 00:56:45,700
but I'm back having
these conversations.

1328
00:56:45,700 --> 00:56:49,250
And then you present a
different side when you're here.

1329
00:56:49,250 --> 00:56:55,090
I've heard that a lot, that
sometimes being at a top place,

1330
00:56:55,090 --> 00:56:58,060
people feeling
uncomfortable going home,

1331
00:56:58,060 --> 00:57:01,360
that the family members
just feel like they

1332
00:57:01,360 --> 00:57:03,027
don't want to sound
stupid, so they just

1333
00:57:03,027 --> 00:57:04,610
don't know what to
say to you anymore.

1334
00:57:04,610 --> 00:57:05,730
It's like, it's still me.

1335
00:57:05,730 --> 00:57:09,070
It's OK. (LAUGHS) That's great.

1336
00:57:09,070 --> 00:57:11,110
OK.

1337
00:57:11,110 --> 00:57:11,830
Anybody else?

1338
00:57:11,830 --> 00:57:12,430
Yeah, yeah?

1339
00:57:12,430 --> 00:57:15,130
AUDIENCE: Yeah, so
I personally have

1340
00:57:15,130 --> 00:57:17,622
a stereotype that Asian women,
especially those who are,

1341
00:57:17,622 --> 00:57:19,080
quote, unquote,
fresh off the boat,

1342
00:57:19,080 --> 00:57:22,420
are more reserved and quiet.

1343
00:57:22,420 --> 00:57:27,160
And while I'm a person who kind
of confirms that stereotype,

1344
00:57:27,160 --> 00:57:30,520
when I'm with people that I'm
meeting for the first time,

1345
00:57:30,520 --> 00:57:33,300
I try to be a little
more vocal and talkative,

1346
00:57:33,300 --> 00:57:35,260
to give an impression
that I want

1347
00:57:35,260 --> 00:57:37,010
to engage in this conversation.

1348
00:57:37,010 --> 00:57:38,620
And sometimes that
makes you feel bad,

1349
00:57:38,620 --> 00:57:40,060
because I'm not true to myself.

1350
00:57:40,060 --> 00:57:41,060
So, yeah.

1351
00:57:41,060 --> 00:57:42,610
CATHY L. DRENNAN: Yeah, yeah.

1352
00:57:42,610 --> 00:57:44,240
People may not
believe this about me,

1353
00:57:44,240 --> 00:57:45,460
but I used to be really shy.

1354
00:57:45,460 --> 00:57:47,680
[SOFT LAUGHTER]

1355
00:57:47,680 --> 00:57:48,520
So, yeah.

1356
00:57:48,520 --> 00:57:52,220
So all those years of
imposter syndrome--

1357
00:57:52,220 --> 00:57:52,810
all right.

1358
00:57:52,810 --> 00:57:54,940
I'm going to wear
my not-shy clothes,

1359
00:57:54,940 --> 00:57:56,800
and talk like a not-shy person.

1360
00:57:56,800 --> 00:57:58,690
But yeah, I had to really
struggle with that,

1361
00:57:58,690 --> 00:58:01,180
and I think it's very hard.

1362
00:58:01,180 --> 00:58:04,100
When you are worried
about that stereotype,

1363
00:58:04,100 --> 00:58:05,370
it's pushing yourself out.

1364
00:58:05,370 --> 00:58:06,820
Being a little more--

1365
00:58:06,820 --> 00:58:10,360
so that's it that's a
really, really nice example.

1366
00:58:10,360 --> 00:58:11,910
Anyone else?

1367
00:58:11,910 --> 00:58:12,550
Yeah?

1368
00:58:12,550 --> 00:58:14,560
AUDIENCE: Yeah, so I
have several tattoos,

1369
00:58:14,560 --> 00:58:16,030
and I also have a nose piercing.

1370
00:58:16,030 --> 00:58:18,050
And when I'm in
professional settings,

1371
00:58:18,050 --> 00:58:21,580
especially at conferences, I
try to cover up my tattoos,

1372
00:58:21,580 --> 00:58:24,190
and sometimes they even
put in a different nose

1373
00:58:24,190 --> 00:58:26,800
ring that's clear so
you really can't see it

1374
00:58:26,800 --> 00:58:28,180
unless you're close up.

1375
00:58:28,180 --> 00:58:31,480
And I try to do that
in order to not confirm

1376
00:58:31,480 --> 00:58:34,060
a negative stereotype of
people who decide to put art

1377
00:58:34,060 --> 00:58:37,300
on their body or
have extra piercings,

1378
00:58:37,300 --> 00:58:40,840
and to appear more professional,
and that I understand

1379
00:58:40,840 --> 00:58:42,305
and know what I'm talking about.

1380
00:58:42,305 --> 00:58:44,680
CATHY L. DRENNAN: Yeah, that's
also a really-- everyone's

1381
00:58:44,680 --> 00:58:47,680
got really great examples.

1382
00:58:47,680 --> 00:58:50,190
That has kind of come up in
[INAUDIBLE] really thinking

1383
00:58:50,190 --> 00:58:51,940
about how you're dressed
and other things.

1384
00:58:51,940 --> 00:58:56,020
And one time I had
videotapes to use

1385
00:58:56,020 --> 00:58:58,360
in freshman chemistry, of
scientists talking about how

1386
00:58:58,360 --> 00:58:59,645
they were using chemistry.

1387
00:58:59,645 --> 00:59:01,270
And I got all this
feedback because one

1388
00:59:01,270 --> 00:59:04,330
of the people in the video
had piercings and tattoos.

1389
00:59:04,330 --> 00:59:07,690
And they're like, but that's
not a scientist, right?

1390
00:59:07,690 --> 00:59:09,610
But then he talked
so articulately,

1391
00:59:09,610 --> 00:59:11,260
and it was mind-blowing.

1392
00:59:11,260 --> 00:59:13,210
But we do have
these ideas of what

1393
00:59:13,210 --> 00:59:15,130
scientists should look like.

1394
00:59:15,130 --> 00:59:17,020
I partly blame Hollywood.

1395
00:59:17,020 --> 00:59:20,800
Polyester pants, and the lab
coat, and pocket protectors.

1396
00:59:20,800 --> 00:59:24,410
And it turns out scientists
look like everyone else.

1397
00:59:24,410 --> 00:59:26,470
But then we worry,
and sometimes try

1398
00:59:26,470 --> 00:59:31,030
to fit in, and whistle a
little Vivaldi to hide maybe

1399
00:59:31,030 --> 00:59:31,990
some parts of that.

1400
00:59:31,990 --> 00:59:33,340
And so, yeah.

1401
00:59:33,340 --> 00:59:35,350
Really, really good
examples everyone.

1402
00:59:35,350 --> 00:59:35,980
OK.

1403
00:59:35,980 --> 00:59:37,780
Let's move on.

1404
00:59:37,780 --> 00:59:39,760
Who has stereotypes?

1405
00:59:39,760 --> 00:59:41,112
ENTIRE AUDIENCE: Everyone!

1406
00:59:41,112 --> 00:59:42,070
CATHY L. DRENNAN: Yeah.

1407
00:59:42,070 --> 00:59:43,000
Sorry.

1408
00:59:43,000 --> 00:59:44,790
Sorry for the bad news.

1409
00:59:44,790 --> 00:59:47,440
But let me show you some data
in case you're not convinced.

1410
00:59:47,440 --> 00:59:47,940
All right.

1411
00:59:47,940 --> 00:59:50,200
So there's a whole
bunch of studies,

1412
00:59:50,200 --> 00:59:52,030
and I'm just going to
tell you about a few

1413
00:59:52,030 --> 00:59:56,410
of them that get at
this fact that we all

1414
00:59:56,410 --> 00:59:57,670
do have stereotypes.

1415
00:59:57,670 --> 01:00:00,490
Because we all live in a
culture, we're all part of it.

1416
01:00:00,490 --> 01:00:03,160
We are, in fact, not in
the cold room for 36 hours.

1417
01:00:03,160 --> 01:00:07,250
We come out to socialize with
other people at some point,

1418
01:00:07,250 --> 01:00:10,730
and so we develop stereotypes.

1419
01:00:10,730 --> 01:00:11,230
All right.

1420
01:00:11,230 --> 01:00:13,450
So there are these a
bunch of different studies

1421
01:00:13,450 --> 01:00:17,200
where they show groups,
multiple different CVs.

1422
01:00:17,200 --> 01:00:19,510
And they have one
CV that's clearly

1423
01:00:19,510 --> 01:00:22,930
a man's name, another CV
that's clearly a woman's name,

1424
01:00:22,930 --> 01:00:25,330
and then they ask this
group, pick the best

1425
01:00:25,330 --> 01:00:26,830
candidate for something.

1426
01:00:26,830 --> 01:00:29,060
And the data show
that if the see

1427
01:00:29,060 --> 01:00:31,720
these are designed to
be very, very similar--

1428
01:00:31,720 --> 01:00:34,180
similar kinds of
schools, similar numbers

1429
01:00:34,180 --> 01:00:36,550
of publications,
similar awards--

1430
01:00:36,550 --> 01:00:40,270
that people will pick the
CV with the man's name

1431
01:00:40,270 --> 01:00:41,710
on as being better.

1432
01:00:41,710 --> 01:00:44,800
And importantly,
women do this too.

1433
01:00:44,800 --> 01:00:47,980
So this idea of, let's put
a woman on every committee

1434
01:00:47,980 --> 01:00:52,120
so that we make sure that this
committee is doing the best,

1435
01:00:52,120 --> 01:00:54,610
picking the best
candidate, whatever, women

1436
01:00:54,610 --> 01:00:56,370
do the exact same thing as men.

1437
01:00:56,370 --> 01:00:58,570
They also pick
the male candidate

1438
01:00:58,570 --> 01:01:01,240
when the CVs are very similar.

1439
01:01:01,240 --> 01:01:06,310
So a lot of these studies were
done in the English profession,

1440
01:01:06,310 --> 01:01:10,180
but Joe Handelsman and a bunch
of others in this publication,

1441
01:01:10,180 --> 01:01:16,030
in PNAS in 2012, redid
did this CV study,

1442
01:01:16,030 --> 01:01:18,130
but it was only for scientists.

1443
01:01:18,130 --> 01:01:22,270
So people would say,
scientists are logical, right?

1444
01:01:22,270 --> 01:01:24,160
We can't have stereotypes.

1445
01:01:24,160 --> 01:01:25,570
We're about facts.

1446
01:01:25,570 --> 01:01:26,350
We're about data.

1447
01:01:26,350 --> 01:01:29,860
We are so logical
we can't possibly

1448
01:01:29,860 --> 01:01:31,330
really have stereotypes.

1449
01:01:31,330 --> 01:01:33,550
And so, clear
convincing evidence

1450
01:01:33,550 --> 01:01:35,380
that that is, in fact, not true.

1451
01:01:35,380 --> 01:01:37,660
Scientists are just like
all other human beings,

1452
01:01:37,660 --> 01:01:38,600
as it turns out.

1453
01:01:38,600 --> 01:01:40,730
They're people.

1454
01:01:40,730 --> 01:01:42,800
Anyhow, so this study.

1455
01:01:42,800 --> 01:01:43,580
Loved this study.

1456
01:01:43,580 --> 01:01:44,540
Jealous of this study.

1457
01:01:44,540 --> 01:01:46,860
Why did I not do this study?

1458
01:01:46,860 --> 01:01:48,410
Anyhow.

1459
01:01:48,410 --> 01:01:52,010
So what they did is they
took this resume, CV, said it

1460
01:01:52,010 --> 01:01:54,140
was for a lab manager position.

1461
01:01:54,140 --> 01:01:56,840
They had the name John on
it or the name Jennifer,

1462
01:01:56,840 --> 01:01:58,460
and they sent it to
different people,

1463
01:01:58,460 --> 01:02:01,820
and then saw what people
thought collectively about John,

1464
01:02:01,820 --> 01:02:05,120
and what people thought
collectively about Jennifer.

1465
01:02:05,120 --> 01:02:09,170
Exactly the same
CV, so they should

1466
01:02:09,170 --> 01:02:11,960
have thought the same
things about both of them,

1467
01:02:11,960 --> 01:02:15,170
because it was the same.

1468
01:02:15,170 --> 01:02:20,690
But then they compared for
the male name versus female

1469
01:02:20,690 --> 01:02:23,390
who's applying for this
lab manager position,

1470
01:02:23,390 --> 01:02:27,010
they thought that the
man was more competent,

1471
01:02:27,010 --> 01:02:29,920
that the man was more
hireable, and was

1472
01:02:29,920 --> 01:02:32,470
going to be easier
to mentor, and also

1473
01:02:32,470 --> 01:02:34,480
should get pay to lot more.

1474
01:02:34,480 --> 01:02:38,230
Now, again, let me emphasize,
these are identical documents,

1475
01:02:38,230 --> 01:02:40,120
except one has the
name John on it,

1476
01:02:40,120 --> 01:02:42,440
and one had the
name Jennifer on it.

1477
01:02:42,440 --> 01:02:46,300
And they looked at how men
viewed this and how women did.

1478
01:02:46,300 --> 01:02:49,330
It was male faculty,
women faculty, again,

1479
01:02:49,330 --> 01:02:50,980
for a lab manager position.

1480
01:02:50,980 --> 01:02:52,780
Women thought the same thing.

1481
01:02:52,780 --> 01:02:55,330
Women thought that the CV
with the name John on it

1482
01:02:55,330 --> 01:02:58,990
was a lot more impressive than
the CV with the name Jennifer.

1483
01:02:58,990 --> 01:03:03,580
So scientists, the
ivory tower, is just

1484
01:03:03,580 --> 01:03:06,525
locked-in people with
the same prejudices

1485
01:03:06,525 --> 01:03:07,650
that you find other places.

1486
01:03:07,650 --> 01:03:09,460
The same stereotypes.

1487
01:03:09,460 --> 01:03:10,350
All right.

1488
01:03:10,350 --> 01:03:10,850
OK.

1489
01:03:10,850 --> 01:03:15,010
So we need some good news.

1490
01:03:15,010 --> 01:03:16,690
I think it's time for good news.

1491
01:03:16,690 --> 01:03:18,820
I've convinced you
that you can all

1492
01:03:18,820 --> 01:03:23,740
be victims of stereotype threat,
that you all have stereotypes.

1493
01:03:23,740 --> 01:03:27,040
Probably, you're all worried
about imposter syndrome.

1494
01:03:27,040 --> 01:03:30,733
If you didn't have it
before, I'm sure you do now.

1495
01:03:30,733 --> 01:03:31,900
But you know that I have it.

1496
01:03:31,900 --> 01:03:34,970
Maybe that means like--

1497
01:03:34,970 --> 01:03:36,230
anyway.

1498
01:03:36,230 --> 01:03:38,230
Good news.

1499
01:03:38,230 --> 01:03:40,270
Wise criticism.

1500
01:03:40,270 --> 01:03:42,960
So this can be used to mitigate
the effects of stereotype

1501
01:03:42,960 --> 01:03:43,590
threat.

1502
01:03:43,590 --> 01:03:46,950
And again, it's criticism
that's delivered in such a way

1503
01:03:46,950 --> 01:03:50,970
that students feel like
they're capable of a high level

1504
01:03:50,970 --> 01:03:54,390
of achievement, that
students are less defensive

1505
01:03:54,390 --> 01:03:56,500
and feel less threatened.

1506
01:03:56,500 --> 01:04:03,510
And so, here, again, it's not
about whether there's criticism

1507
01:04:03,510 --> 01:04:08,310
or not, it's really about how
that criticism is delivered.

1508
01:04:08,310 --> 01:04:10,710
So I'll just give
you a brief example

1509
01:04:10,710 --> 01:04:12,150
what I think about here.

1510
01:04:12,150 --> 01:04:15,080
When I started as
Assistant Professor,

1511
01:04:15,080 --> 01:04:17,520
it was the first time I was
writing a paper from my lab

1512
01:04:17,520 --> 01:04:21,570
that my advisor's not
deciding it's OK or not.

1513
01:04:21,570 --> 01:04:22,740
This is for me.

1514
01:04:22,740 --> 01:04:24,900
So I gave it to one of
my senior colleagues

1515
01:04:24,900 --> 01:04:27,630
to critique before
I sent it out.

1516
01:04:27,630 --> 01:04:33,360
It came back covered in red pen.

1517
01:04:33,360 --> 01:04:37,810
[INAUDIBLE] And I was just
thinking, what was I thinking?

1518
01:04:37,810 --> 01:04:38,980
I can't be a professor.

1519
01:04:38,980 --> 01:04:40,540
This is the most
ridiculous thing.

1520
01:04:40,540 --> 01:04:44,570
How did I ever think that
I could possibly do this?

1521
01:04:44,570 --> 01:04:47,080
But then I got a little
perspective on it.

1522
01:04:47,080 --> 01:04:51,790
I realized that this
professor had taken time

1523
01:04:51,790 --> 01:04:56,560
to not only read the paper
through and make comments,

1524
01:04:56,560 --> 01:05:00,430
but there were notes about
reading the background

1525
01:05:00,430 --> 01:05:02,310
literature on the paper.

1526
01:05:02,310 --> 01:05:08,050
This wasn't their field, so
they had read all the references

1527
01:05:08,050 --> 01:05:11,140
and thought about how I could
have improved the paper.

1528
01:05:11,140 --> 01:05:13,450
Who has time to do that?

1529
01:05:13,450 --> 01:05:15,460
The only person who
has time to do that

1530
01:05:15,460 --> 01:05:19,660
is someone who is completely
and totally dedicated

1531
01:05:19,660 --> 01:05:22,160
to my success.

1532
01:05:22,160 --> 01:05:24,160
So it was criticism.

1533
01:05:24,160 --> 01:05:25,420
It was criticism.

1534
01:05:25,420 --> 01:05:26,260
There was criticism!

1535
01:05:26,260 --> 01:05:28,360
[LAUGHTER]

1536
01:05:28,360 --> 01:05:31,900
But it was criticism that
was delivered because they

1537
01:05:31,900 --> 01:05:34,360
believed I could
do this, and wanted

1538
01:05:34,360 --> 01:05:38,290
to help me, put their money
where their mouth was,

1539
01:05:38,290 --> 01:05:41,380
spend time helping
me be successful.

1540
01:05:41,380 --> 01:05:44,860
So next time you get back
a draft covered with red,

1541
01:05:44,860 --> 01:05:49,090
don't think of it as
harsh, I can't do it,

1542
01:05:49,090 --> 01:05:52,490
blood of a massacre of
the paper spewing out.

1543
01:05:52,490 --> 01:05:54,490
You think about it as love.

1544
01:05:54,490 --> 01:05:57,160
[LAUGHTER]

1545
01:05:57,160 --> 01:05:58,450
Criticism takes time.

1546
01:05:58,450 --> 01:05:59,920
It's love!

1547
01:05:59,920 --> 01:06:05,410
Red love pouring out
all over the paper.

1548
01:06:05,410 --> 01:06:07,600
Criticism is
important, but the way

1549
01:06:07,600 --> 01:06:10,210
it's delivered-- if you
feel it's delivered in a way

1550
01:06:10,210 --> 01:06:13,270
that the person really
believes in you,

1551
01:06:13,270 --> 01:06:16,990
then it can really
motivate you to do better.

1552
01:06:16,990 --> 01:06:19,810
So criticism is good,
but how you deliver

1553
01:06:19,810 --> 01:06:21,260
it makes a huge difference.

1554
01:06:21,260 --> 01:06:21,760
All right.

1555
01:06:21,760 --> 01:06:25,150
So Mary O'Reilly who is a
graduate student here at MIT,

1556
01:06:25,150 --> 01:06:27,550
did all these really
cool little cartoons.

1557
01:06:27,550 --> 01:06:30,900
Little call out to Mary, now.

1558
01:06:30,900 --> 01:06:33,467
You're a student now, but
think about what you can do.

1559
01:06:33,467 --> 01:06:35,800
You don't have to sacrifice
completely for your science.

1560
01:06:35,800 --> 01:06:38,200
You can do art on the side.

1561
01:06:38,200 --> 01:06:41,410
So Mary had a hobby and
it's put to good use.

1562
01:06:41,410 --> 01:06:44,220
So which response
would you rather hear

1563
01:06:44,220 --> 01:06:45,780
if you did poorly on a test?

1564
01:06:45,780 --> 01:06:47,340
And I don't know if
you can see this.

1565
01:06:47,340 --> 01:06:51,000
This is a C-, which is not a
good score for someone who is

1566
01:06:51,000 --> 01:06:54,090
a high achiever.

1567
01:06:54,090 --> 01:06:56,910
Undeserved praise
can be super harmful.

1568
01:06:56,910 --> 01:06:59,290
"This is a good score for you.

1569
01:06:59,290 --> 01:07:02,010
You should be pleased."

1570
01:07:02,010 --> 01:07:03,840
What is this person
telling this person

1571
01:07:03,840 --> 01:07:05,805
by giving them that praise?

1572
01:07:08,933 --> 01:07:10,100
That's the best they can do.

1573
01:07:10,100 --> 01:07:12,460
You should be happy with a C-.

1574
01:07:12,460 --> 01:07:12,960
No.

1575
01:07:12,960 --> 01:07:16,163
No one should be
happy with a C-.

1576
01:07:16,163 --> 01:07:18,080
Of course, if it's pass,
no record, and you're

1577
01:07:18,080 --> 01:07:20,270
just shooting for that
pass-- but, no, no, no.

1578
01:07:20,270 --> 01:07:21,680
No one should be
happy with that.

1579
01:07:21,680 --> 01:07:21,920
All right.

1580
01:07:21,920 --> 01:07:23,462
So you have to be
careful about that.

1581
01:07:23,462 --> 01:07:26,390
The worst thing you can do is
send the message to someone

1582
01:07:26,390 --> 01:07:29,275
that they messed up, but that's
all that it can be expected

1583
01:07:29,275 --> 01:07:32,180
of you is to mess up.

1584
01:07:32,180 --> 01:07:33,170
Bad criticism.

1585
01:07:33,170 --> 01:07:34,790
We may have all received that.

1586
01:07:34,790 --> 01:07:38,480
Yeah, you'll have to try
harder if you want to pass.

1587
01:07:38,480 --> 01:07:41,630
No feedback can also
be harmful, because you

1588
01:07:41,630 --> 01:07:44,180
might have this idea that
you don't belong here.

1589
01:07:44,180 --> 01:07:47,480
And if no one's countering that
and telling you you do belong,

1590
01:07:47,480 --> 01:07:50,720
then you're just taking
that message away.

1591
01:07:50,720 --> 01:07:55,010
And wise criticism can be,
again, the most powerful thing.

1592
01:07:55,010 --> 01:07:57,710
Saying, hey, I know
you were doing really

1593
01:07:57,710 --> 01:07:58,790
well in recitation.

1594
01:07:58,790 --> 01:08:00,440
I'm not sure what
happened on the exam,

1595
01:08:00,440 --> 01:08:02,930
but let's sit together
and try to figure out.

1596
01:08:02,930 --> 01:08:04,790
You did this part really well.

1597
01:08:04,790 --> 01:08:07,370
You just need to figure out how
to demonstrate your knowledge

1598
01:08:07,370 --> 01:08:09,540
on this other part of the exam.

1599
01:08:09,540 --> 01:08:12,980
So just a brief story on this.

1600
01:08:12,980 --> 01:08:15,110
Some of my friends who
are professors, parents

1601
01:08:15,110 --> 01:08:15,933
call them up.

1602
01:08:15,933 --> 01:08:17,600
Do you know how much
tuition I'm paying?

1603
01:08:17,600 --> 01:08:19,410
Why did my kid not get an A?

1604
01:08:19,410 --> 01:08:21,439
I have never gotten
that phone call here,

1605
01:08:21,439 --> 01:08:24,810
partly because everybody knows
that MIT is a tough place,

1606
01:08:24,810 --> 01:08:27,380
and so, they don't send
their kid with the idea

1607
01:08:27,380 --> 01:08:31,550
that the money in tuition is
guaranteeing any kind of grade.

1608
01:08:31,550 --> 01:08:34,100
But I did get a phone
call from a parent once.

1609
01:08:34,100 --> 01:08:36,109
And they called
me and they said,

1610
01:08:36,109 --> 01:08:38,010
I just had to share
with you this story,

1611
01:08:38,010 --> 01:08:40,100
it's about my daughter
who's in your class.

1612
01:08:40,100 --> 01:08:43,460
They said, first
exam comes in MIT,

1613
01:08:43,460 --> 01:08:45,319
and she doesn't do that well.

1614
01:08:45,319 --> 01:08:50,850
And she calls me, and she's
crying, and she's crying a lot.

1615
01:08:50,850 --> 01:08:52,680
And she said, dad,
I really studied.

1616
01:08:52,680 --> 01:08:54,460
I'm telling you,
I really studied.

1617
01:08:54,460 --> 01:08:57,810
I thought I knew this material,
but this was my grade.

1618
01:08:57,810 --> 01:09:01,069
And I don't know what happened,
I really thought I knew it.

1619
01:09:01,069 --> 01:09:03,300
And she's like, can
you come and get me?

1620
01:09:03,300 --> 01:09:05,330
He's like, the first exam.

1621
01:09:05,330 --> 01:09:07,740
It's still September, right?

1622
01:09:07,740 --> 01:09:09,410
And so, he doesn't
know what to do,

1623
01:09:09,410 --> 01:09:14,380
and he says, well, I'm going
to come get you immediately.

1624
01:09:14,380 --> 01:09:18,330
Let's just see how you
feel in a little bit.

1625
01:09:18,330 --> 01:09:20,300
So then he gets a call
back a few hours later.

1626
01:09:20,300 --> 01:09:21,700
And you say, Dad!

1627
01:09:21,700 --> 01:09:22,370
Dad!

1628
01:09:22,370 --> 01:09:22,970
Dad!

1629
01:09:22,970 --> 01:09:25,399
My TA emailed me!

1630
01:09:25,399 --> 01:09:28,790
They said, I know you
know the material better

1631
01:09:28,790 --> 01:09:30,470
than your grade on the test.

1632
01:09:30,470 --> 01:09:31,670
I told you, dad!

1633
01:09:31,670 --> 01:09:34,279
I told you that I knew
the material better.

1634
01:09:34,279 --> 01:09:35,840
And the TA knew I knew!

1635
01:09:35,840 --> 01:09:37,520
I knew I knew the
material better!

1636
01:09:37,520 --> 01:09:39,050
He said, you can
come in and talk

1637
01:09:39,050 --> 01:09:40,772
about test-taking strategies.

1638
01:09:40,772 --> 01:09:43,189
And so, the dad said, so I
don't need to come and get you?

1639
01:09:43,189 --> 01:09:44,590
She's like, no, I'm good.

1640
01:09:44,590 --> 01:09:45,500
Right?

1641
01:09:45,500 --> 01:09:52,520
So a little bit of the
message that that TA sent,

1642
01:09:52,520 --> 01:09:54,350
saying, I know that
you know this better.

1643
01:09:54,350 --> 01:09:56,480
Let's get together and
figure out what we can do.

1644
01:09:56,480 --> 01:10:00,030
It was everything
to this one person.

1645
01:10:00,030 --> 01:10:02,860
So wise criticism, very helpful.

1646
01:10:02,860 --> 01:10:06,210
He still said, yeah, that
wasn't a good performance, but I

1647
01:10:06,210 --> 01:10:07,480
you can do better.

1648
01:10:07,480 --> 01:10:08,550
OK.

1649
01:10:08,550 --> 01:10:12,467
So I just want to emphasize that
no feedback is not a solution.

1650
01:10:12,467 --> 01:10:14,550
Sometimes having these
conversations of criticism,

1651
01:10:14,550 --> 01:10:16,210
ugh.

1652
01:10:16,210 --> 01:10:18,810
So the easiest route
is say nothing.

1653
01:10:18,810 --> 01:10:20,390
But that is not a good route.

1654
01:10:20,390 --> 01:10:22,680
And let me just try to
convince you of that.

1655
01:10:22,680 --> 01:10:27,120
In doing so, we have some
more cool Mary cartoons.

1656
01:10:27,120 --> 01:10:29,550
And we're just going to leave
the classroom for a minute.

1657
01:10:29,550 --> 01:10:31,710
In this scenario,
we have a professor

1658
01:10:31,710 --> 01:10:35,100
coming in, yelling
at this male student,

1659
01:10:35,100 --> 01:10:37,080
and saying nothing to
this female student who

1660
01:10:37,080 --> 01:10:38,700
is sitting right next to him.

1661
01:10:38,700 --> 01:10:40,642
So the professor comes
in, said, you should

1662
01:10:40,642 --> 01:10:41,850
have had this done yesterday!

1663
01:10:41,850 --> 01:10:43,620
I'm giving a talk
next week in Texas,

1664
01:10:43,620 --> 01:10:46,292
and I need to have
these PowerPoint slides!

1665
01:10:46,292 --> 01:10:48,000
Why haven't you finished
this experiment?

1666
01:10:48,000 --> 01:10:49,200
I have to get going!

1667
01:10:49,200 --> 01:10:50,950
And then he leaves the lab.

1668
01:10:50,950 --> 01:10:54,280
And so, in the little blurb
here from the female students

1669
01:10:54,280 --> 01:10:56,410
thoughts he can't
hear, she said,

1670
01:10:56,410 --> 01:11:00,070
I wish she cared as
much about my project.

1671
01:11:00,070 --> 01:11:02,550
It's just not fair.

1672
01:11:02,550 --> 01:11:04,830
And then the man is
thinking, he never

1673
01:11:04,830 --> 01:11:07,020
yells at the girls like
that, and her project

1674
01:11:07,020 --> 01:11:08,760
isn't going any
better than mine.

1675
01:11:08,760 --> 01:11:10,620
It's just not fair.

1676
01:11:10,620 --> 01:11:14,970
So the woman is upset about
not getting yelled at,

1677
01:11:14,970 --> 01:11:17,070
which sort of seems
counterintuitive at first,

1678
01:11:17,070 --> 01:11:20,430
until you realize
that the one graduate

1679
01:11:20,430 --> 01:11:22,020
student's getting
yelled at because he

1680
01:11:22,020 --> 01:11:25,110
wants his data for a talk.

1681
01:11:25,110 --> 01:11:27,600
So he's never asking the
female student for her data

1682
01:11:27,600 --> 01:11:28,440
for a talk.

1683
01:11:28,440 --> 01:11:30,960
He's never really talking to
her at all about her research.

1684
01:11:30,960 --> 01:11:34,320
It's not going well, and
he doesn't even care.

1685
01:11:34,320 --> 01:11:37,560
She is apparently so
insignificant to him.

1686
01:11:37,560 --> 01:11:40,560
She's too insignificant, her
research is so unimportant,

1687
01:11:40,560 --> 01:11:42,552
he doesn't even have
time to yell at her.

1688
01:11:42,552 --> 01:11:44,010
He's not interest
in yelling at her

1689
01:11:44,010 --> 01:11:46,343
because he's not going to use
her research for anything.

1690
01:11:46,343 --> 01:11:50,130
He has no expectations that she
would ever deliver something

1691
01:11:50,130 --> 01:11:52,050
that would be talk worthy.

1692
01:11:52,050 --> 01:11:54,000
That's at least
what's in her mind.

1693
01:11:54,000 --> 01:11:56,250
Meanwhile, the male professor's
very proud of himself

1694
01:11:56,250 --> 01:11:58,260
for never yelling at the girls.

1695
01:11:58,260 --> 01:12:00,930
And the male students are really
angry that the girls don't

1696
01:12:00,930 --> 01:12:03,630
get yelled at, not
realizing that he's

1697
01:12:03,630 --> 01:12:04,840
paying some attention.

1698
01:12:04,840 --> 01:12:07,090
He actually cares if
they're getting results.

1699
01:12:07,090 --> 01:12:12,480
So this is a lose-lose
situation for everybody.

1700
01:12:12,480 --> 01:12:17,040
So along these lines I was
called on to, at one point,

1701
01:12:17,040 --> 01:12:19,590
interview all of the
female graduate students

1702
01:12:19,590 --> 01:12:21,840
in the chemistry department,
and all the male students

1703
01:12:21,840 --> 01:12:23,640
in the chemistry
department, to figure out

1704
01:12:23,640 --> 01:12:25,265
how they figured out
whether they would

1705
01:12:25,265 --> 01:12:27,180
apply for academic positions.

1706
01:12:27,180 --> 01:12:31,620
And I was given this task
because the department was

1707
01:12:31,620 --> 01:12:34,950
very low on the list of the
number of female graduate

1708
01:12:34,950 --> 01:12:37,250
students who were applying
for faculty positions.

1709
01:12:37,250 --> 01:12:40,470
So they wanted to figure out
why the women weren't applying.

1710
01:12:40,470 --> 01:12:44,320
And who better to figure
that out than me, a woman.

1711
01:12:44,320 --> 01:12:44,820
All right.

1712
01:12:44,820 --> 01:12:47,070
So I actually was
kind of curious

1713
01:12:47,070 --> 01:12:48,510
to see what people would say.

1714
01:12:48,510 --> 01:12:51,540
So again, this is not
statistically significant.

1715
01:12:51,540 --> 01:12:53,910
I've collected some
data over the years that

1716
01:12:53,910 --> 01:12:55,585
is statistically significant.

1717
01:12:55,585 --> 01:12:57,210
This was just
conversations that I had,

1718
01:12:57,210 --> 01:12:59,070
but I just wanted to
share this with you.

1719
01:12:59,070 --> 01:13:00,930
So I asked both
men and women, how

1720
01:13:00,930 --> 01:13:03,940
do you know if you're good
enough to be a professor?

1721
01:13:03,940 --> 01:13:07,380
And both said, I don't know.

1722
01:13:07,380 --> 01:13:08,110
It was the same.

1723
01:13:08,110 --> 01:13:09,420
It was the same answer.

1724
01:13:09,420 --> 01:13:10,990
They both said, I don't know.

1725
01:13:10,990 --> 01:13:13,080
And it's like, well,
what do you do with that?

1726
01:13:13,080 --> 01:13:16,590
And the women said, well, I wait
for the professor to tell me

1727
01:13:16,590 --> 01:13:17,670
that I'm good enough.

1728
01:13:17,670 --> 01:13:20,750
And if they don't tell
me, I assume I'm not.

1729
01:13:20,750 --> 01:13:24,680
The men said, well,
I go ahead and apply,

1730
01:13:24,680 --> 01:13:26,590
and if I'm not good
enough, the professor

1731
01:13:26,590 --> 01:13:29,250
is likely to tell me
that at some point.

1732
01:13:29,250 --> 01:13:33,140
So meanwhile, the
professor is saying nothing

1733
01:13:33,140 --> 01:13:35,660
to either group about
whether they're good enough.

1734
01:13:35,660 --> 01:13:38,480
The women are taking that
as a message they're not.

1735
01:13:38,480 --> 01:13:40,020
The men are saying,
I'm just going

1736
01:13:40,020 --> 01:13:42,920
to go ahead and try
until someone stops me.

1737
01:13:42,920 --> 01:13:46,370
So no feedback, you
might be sending

1738
01:13:46,370 --> 01:13:47,900
messages you don't intend.

1739
01:13:47,900 --> 01:13:51,350
I don't think these
professors were intending

1740
01:13:51,350 --> 01:13:53,330
to send message to
the female students

1741
01:13:53,330 --> 01:13:54,710
that they weren't good enough.

1742
01:13:54,710 --> 01:13:57,650
They just were busy doing
stuff and didn't say anything

1743
01:13:57,650 --> 01:13:58,490
to anyone.

1744
01:13:58,490 --> 01:14:02,060
So it's a reminder to
give feedback to people.

1745
01:14:02,060 --> 01:14:03,800
Because if there's
no feedback, you

1746
01:14:03,800 --> 01:14:07,760
don't know what the individual
is taking away from that.

1747
01:14:07,760 --> 01:14:10,250
In the absence of
it, own insecurities

1748
01:14:10,250 --> 01:14:12,570
will often come into play.

1749
01:14:12,570 --> 01:14:13,190
All right.

1750
01:14:13,190 --> 01:14:18,213
So other things that one can
do is change the narrative.

1751
01:14:18,213 --> 01:14:19,880
Because, remember,
as I was just saying,

1752
01:14:19,880 --> 01:14:23,030
people have their own
insecurities going.

1753
01:14:23,030 --> 01:14:25,460
And so, sometimes
you want to let

1754
01:14:25,460 --> 01:14:29,690
them know that perhaps other
people have had insecurities

1755
01:14:29,690 --> 01:14:35,280
as well, and that it's OK,
and that failing sometimes

1756
01:14:35,280 --> 01:14:37,850
is not only OK, it's
part of the process.

1757
01:14:37,850 --> 01:14:41,750
So for high achievers, it's
important to reassure them

1758
01:14:41,750 --> 01:14:45,630
that everyone has had difficulty
at some point in their career.

1759
01:14:45,630 --> 01:14:47,270
So this serves as a
counter narrative,

1760
01:14:47,270 --> 01:14:50,150
this story they're telling
themselves, that somehow you

1761
01:14:50,150 --> 01:14:52,310
have to be perfect
all the time to really

1762
01:14:52,310 --> 01:14:53,940
be successful in science.

1763
01:14:53,940 --> 01:14:57,650
You don't have to be, but we
need to share those stories.

1764
01:14:57,650 --> 01:15:00,860
The CV, it's a
list of successes.

1765
01:15:00,860 --> 01:15:01,780
It's a list of papers.

1766
01:15:01,780 --> 01:15:03,170
It's a list of awards.

1767
01:15:03,170 --> 01:15:08,310
It doesn't say on there, this is
what I've done with my career.

1768
01:15:08,310 --> 01:15:09,020
No.

1769
01:15:09,020 --> 01:15:10,820
What you've done
with your career,

1770
01:15:10,820 --> 01:15:14,390
between every success
are a ton of failures.

1771
01:15:14,390 --> 01:15:17,240
But we don't list the
things that don't work,

1772
01:15:17,240 --> 01:15:19,118
we just list the
things that do work.

1773
01:15:19,118 --> 01:15:21,410
Our publications are full of
the experiments that work,

1774
01:15:21,410 --> 01:15:23,300
not the experiments
that didn't work.

1775
01:15:23,300 --> 01:15:25,220
So it's really easy
for people to look

1776
01:15:25,220 --> 01:15:26,930
at someone who's
successful and think,

1777
01:15:26,930 --> 01:15:28,340
they've never had to struggle.

1778
01:15:28,340 --> 01:15:30,360
It's been super easy for them.

1779
01:15:30,360 --> 01:15:31,700
They've never failed.

1780
01:15:31,700 --> 01:15:33,510
And that is just not the case.

1781
01:15:33,510 --> 01:15:36,080
So if we share those
stories with the students,

1782
01:15:36,080 --> 01:15:39,710
it can help them realize that
maybe this bad grade isn't

1783
01:15:39,710 --> 01:15:40,950
the end of the world.

1784
01:15:40,950 --> 01:15:41,910
It's all OK.

1785
01:15:41,910 --> 01:15:44,920
It's part of the process.

1786
01:15:44,920 --> 01:15:45,420
All right.

1787
01:15:45,420 --> 01:15:50,860
So this is the last exercise.

1788
01:15:50,860 --> 01:15:55,330
So recall a moment where a
mentor shared a story with you

1789
01:15:55,330 --> 01:15:58,690
about a time when he or she
struggled with a concept

1790
01:15:58,690 --> 01:16:01,390
or felt inadequate,
but then reached

1791
01:16:01,390 --> 01:16:03,520
a high level of
achievement, or you

1792
01:16:03,520 --> 01:16:05,230
could recall your own story.

1793
01:16:05,230 --> 01:16:07,060
So something that
someone shared with you

1794
01:16:07,060 --> 01:16:10,510
that made a difference, or
perhaps something that you

1795
01:16:10,510 --> 01:16:13,180
could tell your students
this semester if they're

1796
01:16:13,180 --> 01:16:15,910
all of a sudden going, how
did I get into this place?

1797
01:16:15,910 --> 01:16:17,920
I'm not sure that I belong here.

1798
01:16:17,920 --> 01:16:20,080
What could you
share with them that

1799
01:16:20,080 --> 01:16:23,480
would help them move forward?

1800
01:16:23,480 --> 01:16:23,980
All right.

1801
01:16:23,980 --> 01:16:25,510
Find your partners.

1802
01:16:25,510 --> 01:16:27,706
Think about a time.

1803
01:16:27,706 --> 01:16:31,490
AUDIENCE: I think
for a time when--

1804
01:16:31,490 --> 01:16:33,410
at least a story that
was a counter-narrative

1805
01:16:33,410 --> 01:16:37,480
for me was thinking about
my own struggles in my PhD

1806
01:16:37,480 --> 01:16:41,727
where there was a long
string of failures

1807
01:16:41,727 --> 01:16:43,310
where it felt like
nothing was working

1808
01:16:43,310 --> 01:16:45,480
and I wasn't really
getting anywhere.

1809
01:16:45,480 --> 01:16:48,840
And it was really hard
to see the finish line,

1810
01:16:48,840 --> 01:16:52,030
and it got me really down.

1811
01:16:52,030 --> 01:16:57,110
It felt very difficult. I wasn't
as happy as I could have been.

1812
01:16:57,110 --> 01:16:59,630
And I think what really
helped me break out

1813
01:16:59,630 --> 01:17:01,910
of that was hearing
from my own advisor

1814
01:17:01,910 --> 01:17:06,230
that she had had a pretty
long and arduous PhD as well.

1815
01:17:06,230 --> 01:17:09,120
There was a lot of struggle.

1816
01:17:09,120 --> 01:17:11,570
And that those failures came
in the exact same way, where

1817
01:17:11,570 --> 01:17:15,870
they're stacking on
top of each other.

1818
01:17:15,870 --> 01:17:20,120
And it just feels like there
is no end really in sight,

1819
01:17:20,120 --> 01:17:22,580
and maybe that you're not
even good enough for this.

1820
01:17:22,580 --> 01:17:27,980
But seeing how far she made
it, and how much she succeeded

1821
01:17:27,980 --> 01:17:31,190
after that really, really
reassured me, and makes me

1822
01:17:31,190 --> 01:17:34,730
feel better about
the whole thing.

1823
01:17:34,730 --> 01:17:37,430
It makes you realize
everything's going to be OK.

1824
01:17:37,430 --> 01:17:39,240
This is not atypical.

1825
01:17:39,240 --> 01:17:41,870
It doesn't say something
about me, personally.

1826
01:17:46,430 --> 01:17:48,110
Really what's
reassuring is that it

1827
01:17:48,110 --> 01:17:49,652
feels like everything
is really going

1828
01:17:49,652 --> 01:17:51,720
to work out at the end of it.

1829
01:17:51,720 --> 01:17:55,700
And that changed my entire
attitude really about my PhD.

1830
01:17:55,700 --> 01:17:59,410
I feel tremendously better
about everything since then.

1831
01:17:59,410 --> 01:18:01,052
AUDIENCE: That's very inspiring.

1832
01:18:01,052 --> 01:18:01,760
AUDIENCE: Mm-hmm.

1833
01:18:01,760 --> 01:18:05,150
AUDIENCE: Yeah, for me, I felt
it a lot during undergrad,

1834
01:18:05,150 --> 01:18:07,850
and I still feel it
somewhat in grad school,

1835
01:18:07,850 --> 01:18:12,830
but one experience was
even having an opportunity

1836
01:18:12,830 --> 01:18:15,710
to participate in a
summer program at MIT.

1837
01:18:15,710 --> 01:18:18,530
So I went to a
community college,

1838
01:18:18,530 --> 01:18:21,590
and my parents couldn't
afford to send me

1839
01:18:21,590 --> 01:18:23,367
to a top-tier university.

1840
01:18:23,367 --> 01:18:25,450
We didn't have money saved
up for me to go to one,

1841
01:18:25,450 --> 01:18:29,210
and so the advice was to
go to community college

1842
01:18:29,210 --> 01:18:33,320
first, establish your GPA,
get some scholarships,

1843
01:18:33,320 --> 01:18:35,480
and then transfer to
a four-year institute.

1844
01:18:35,480 --> 01:18:37,640
And while I was at
that community college,

1845
01:18:37,640 --> 01:18:41,840
I didn't feel like I would have
any major opportunities to go

1846
01:18:41,840 --> 01:18:46,250
and do research, especially
at an institute like MIT.

1847
01:18:46,250 --> 01:18:49,370
And I came across the
application for the MIT summer

1848
01:18:49,370 --> 01:18:52,520
research program
through another program

1849
01:18:52,520 --> 01:18:54,770
that I was involved in
at my community college.

1850
01:18:54,770 --> 01:18:56,637
And I was like, OK,
I'll just apply to it.

1851
01:18:56,637 --> 01:18:58,220
They'll probably not
accept me or even

1852
01:18:58,220 --> 01:19:00,137
consider my application,
but I'll still do it.

1853
01:19:00,137 --> 01:19:01,130
And I did it.

1854
01:19:01,130 --> 01:19:01,923
I applied.

1855
01:19:01,923 --> 01:19:03,590
And I ended up getting
into the program.

1856
01:19:03,590 --> 01:19:07,310
And that one opportunity really
opened a lot of doors for me,

1857
01:19:07,310 --> 01:19:09,320
and now I'm able to be
a graduate student here.

1858
01:19:09,320 --> 01:19:13,430
So, yeah, that was a moment
where I really felt insecure,

1859
01:19:13,430 --> 01:19:16,510
and the whole imposter
syndrome really affected me.

1860
01:19:16,510 --> 01:19:18,655
But I'm happy that I
took that opportunity.

1861
01:19:18,655 --> 01:19:19,350
Mm-hmm.

1862
01:19:19,350 --> 01:19:22,850
AUDIENCE: Yeah, that's
a really awesome story.

1863
01:19:22,850 --> 01:19:25,150
AUDIENCE: Thanks.

1864
01:19:25,150 --> 01:19:28,380
AUDIENCE: Yeah, so
when I started college,

1865
01:19:28,380 --> 01:19:32,000
I got a really, really bad
grade on one of my tests.

1866
01:19:32,000 --> 01:19:34,408
I think it was actually my
second semester of college.

1867
01:19:34,408 --> 01:19:34,950
AUDIENCE: OK.

1868
01:19:34,950 --> 01:19:35,610
AUDIENCE: I called my dad.

1869
01:19:35,610 --> 01:19:37,250
And it was the first
bad test I've ever

1870
01:19:37,250 --> 01:19:38,220
had in my entire life.

1871
01:19:38,220 --> 01:19:39,120
I was so upset.

1872
01:19:39,120 --> 01:19:42,320
I called my dad and I said,
dad, dad, I'm so sorry!

1873
01:19:42,320 --> 01:19:44,320
I'm failing!

1874
01:19:44,320 --> 01:19:45,920
And I had a full-ride
scholarship,

1875
01:19:45,920 --> 01:19:48,645
so I thought I was going to lose
my scholarship because of one

1876
01:19:48,645 --> 01:19:49,145
bad test.

1877
01:19:49,145 --> 01:19:49,980
AUDIENCE: Yeah, I
understand that.

1878
01:19:49,980 --> 01:19:51,772
AUDIENCE: And my dad
said, no, no, it's OK.

1879
01:19:51,772 --> 01:19:53,000
It's totally fine.

1880
01:19:53,000 --> 01:19:54,560
When I was in college--

1881
01:19:54,560 --> 01:19:57,850
the same exact college--

1882
01:19:57,850 --> 01:20:02,630
I failed miserably my first
test in this math class.

1883
01:20:02,630 --> 01:20:05,240
And I just worked
really hard and studied,

1884
01:20:05,240 --> 01:20:07,730
and tried to talk
to people, and made

1885
01:20:07,730 --> 01:20:09,570
sure that I knew exactly
what I was doing.

1886
01:20:09,570 --> 01:20:12,830
And by the end of the
semester, my teacher was like,

1887
01:20:12,830 --> 01:20:14,870
don't even come in for
the final because you're

1888
01:20:14,870 --> 01:20:15,980
going to break the curve.

1889
01:20:15,980 --> 01:20:17,690
So you can do it.

1890
01:20:17,690 --> 01:20:20,280
One bad test is
not the end of you.

1891
01:20:20,280 --> 01:20:23,000
AUDIENCE: Yeah, that's cool.

1892
01:20:23,000 --> 01:20:23,997
I wish my--

1893
01:20:23,997 --> 01:20:25,080
AUDIENCE: It really helps.

1894
01:20:25,080 --> 01:20:26,810
AUDIENCE: Yeah, I can imagine
that would really help.

1895
01:20:26,810 --> 01:20:27,380
Yeah.

1896
01:20:27,380 --> 01:20:32,718
So I guess, growing up, I
didn't have female scientists--

1897
01:20:32,718 --> 01:20:33,260
I don't know.

1898
01:20:33,260 --> 01:20:35,773
The media doesn't really portray
female scientists, slash,

1899
01:20:35,773 --> 01:20:38,190
I didn't know anyone that had
a PhD or anything like that.

1900
01:20:38,190 --> 01:20:40,067
So when I got to college,
I knew I was always

1901
01:20:40,067 --> 01:20:41,900
interested in science,
but I didn't actually

1902
01:20:41,900 --> 01:20:42,840
know what that meant.

1903
01:20:42,840 --> 01:20:45,800
And so, I was
really lucky enough

1904
01:20:45,800 --> 01:20:48,410
to have a wonderful
mentor in undergrad.

1905
01:20:48,410 --> 01:20:52,490
And she, was the department
head of chemistry,

1906
01:20:52,490 --> 01:20:53,700
so that was really awesome.

1907
01:20:53,700 --> 01:20:56,510
But we got really close,
and she would tell me

1908
01:20:56,510 --> 01:20:58,760
about her journey
as a scientist,

1909
01:20:58,760 --> 01:21:02,510
and how she started out
becoming a lecturer,

1910
01:21:02,510 --> 01:21:06,440
and raising a family, and
being a woman in science,

1911
01:21:06,440 --> 01:21:08,990
and what that meant for
people's perception,

1912
01:21:08,990 --> 01:21:10,880
and how she felt like
she never really got

1913
01:21:10,880 --> 01:21:12,890
the respect that she deserved.

1914
01:21:12,890 --> 01:21:15,140
And she would always make
sure that we referred to her

1915
01:21:15,140 --> 01:21:18,770
as doctor, because it
was the fact that she

1916
01:21:18,770 --> 01:21:22,430
had worked so hard for it, and
had not been taken seriously.

1917
01:21:22,430 --> 01:21:25,070
So she shared a
lot of like how she

1918
01:21:25,070 --> 01:21:26,630
didn't feel like
she got the respect

1919
01:21:26,630 --> 01:21:30,110
or deserved to almost be
in this position of power.

1920
01:21:30,110 --> 01:21:32,860
But she was an extremely
successful scientist,

1921
01:21:32,860 --> 01:21:36,110
and did wonderful things, and
was department head of our

1922
01:21:36,110 --> 01:21:36,910
at our school.

1923
01:21:36,910 --> 01:21:39,550
And just really
showed me that even

1924
01:21:39,550 --> 01:21:42,220
if you don't feel
like you can do it,

1925
01:21:42,220 --> 01:21:44,090
or you're not
getting the respect,

1926
01:21:44,090 --> 01:21:47,090
you can still be
really successful.

1927
01:21:47,090 --> 01:21:52,990
And having those conversations
and just hearing her story,

1928
01:21:52,990 --> 01:21:55,480
made me feel like,
OK, I can do this,

1929
01:21:55,480 --> 01:21:59,320
and allowed me to address
like my insecurities.

1930
01:21:59,320 --> 01:22:03,760
And her support where she felt
like I could go to grad school,

1931
01:22:03,760 --> 01:22:06,280
was a main reason why I felt
like I could actually do it.

1932
01:22:06,280 --> 01:22:09,370
Because she talked about,
it wasn't in her plans,

1933
01:22:09,370 --> 01:22:11,050
but she still like
went through it.

1934
01:22:11,050 --> 01:22:13,790
And, like, look,
I'm a Professor now,

1935
01:22:13,790 --> 01:22:17,020
even though I don't necessarily
feel like I should have been,

1936
01:22:17,020 --> 01:22:18,820
or could have at times.

1937
01:22:18,820 --> 01:22:21,410
And so, that was
really awesome to hear

1938
01:22:21,410 --> 01:22:24,340
and really made me feel like
grad school was an option

1939
01:22:24,340 --> 01:22:27,460
before I even got here.

1940
01:22:27,460 --> 01:22:30,100
And before, I didn't even
think about grad school.

1941
01:22:30,100 --> 01:22:31,100
AUDIENCE: That's great.

1942
01:22:31,100 --> 01:22:34,090
I'm so glad that she was
able to bring you here.

1943
01:22:34,090 --> 01:22:35,320
AUDIENCE: She's wonderful.

1944
01:22:35,320 --> 01:22:39,450
AUDIENCE: I think a good
example, in my case is my mom.

1945
01:22:39,450 --> 01:22:44,605
So obviously, her experience
is 30 years ago and in Korea,

1946
01:22:44,605 --> 01:22:47,170
so it might be not that
reflective of how things are

1947
01:22:47,170 --> 01:22:48,590
done in the States right now.

1948
01:22:48,590 --> 01:22:53,200
But basically, it was a lot
harder for her, as a woman,

1949
01:22:53,200 --> 01:22:56,110
to get a job than her
male counterparts,

1950
01:22:56,110 --> 01:22:59,050
because people were
worried that she might quit

1951
01:22:59,050 --> 01:23:01,030
when she gets married,
when she gets pregnant,

1952
01:23:01,030 --> 01:23:02,350
when she has a baby.

1953
01:23:02,350 --> 01:23:06,520
And even after she
got a baby and she

1954
01:23:06,520 --> 01:23:08,710
has continued working
in her workplace,

1955
01:23:08,710 --> 01:23:11,440
she was continuously
judged by other people.

1956
01:23:11,440 --> 01:23:13,510
Because when she
worked hard, people

1957
01:23:13,510 --> 01:23:17,590
were blaming her for not putting
in that much of a dedication

1958
01:23:17,590 --> 01:23:18,940
to her home.

1959
01:23:18,940 --> 01:23:21,940
But if she was not
working hard enough,

1960
01:23:21,940 --> 01:23:24,400
people were like, why are you
like not working hard enough?

1961
01:23:24,400 --> 01:23:26,560
And you should not pay
too much of attention

1962
01:23:26,560 --> 01:23:27,950
to your kids and your family.

1963
01:23:27,950 --> 01:23:28,950
AUDIENCE: You can't win.

1964
01:23:28,950 --> 01:23:29,825
AUDIENCE: Yeah, yeah.

1965
01:23:29,825 --> 01:23:34,840
So she had to put in a
lot more time and effort

1966
01:23:34,840 --> 01:23:38,440
into both her household
and in her workplace

1967
01:23:38,440 --> 01:23:41,110
to fight with the
negative stereotype,

1968
01:23:41,110 --> 01:23:42,410
and not to get blame.

1969
01:23:42,410 --> 01:23:45,940
So yeah, her story
really touched me,

1970
01:23:45,940 --> 01:23:48,910
and just keeping me aware that
those kinds of stereotypes

1971
01:23:48,910 --> 01:23:50,440
are going out there, yeah.

1972
01:23:50,440 --> 01:23:54,780
AUDIENCE: Yeah, I think the one
mentor that's really stuck out

1973
01:23:54,780 --> 01:23:57,682
or has had a big impact on
me, especially in grad school,

1974
01:23:57,682 --> 01:23:58,515
has been my advisor.

1975
01:24:01,480 --> 01:24:06,160
Especially when I came
to MIT, I definitely

1976
01:24:06,160 --> 01:24:09,180
had a stereotype of what an
MIT grad student should be.

1977
01:24:09,180 --> 01:24:10,870
And it was like,
they should only

1978
01:24:10,870 --> 01:24:12,255
be focused on their academics.

1979
01:24:12,255 --> 01:24:12,880
AUDIENCE: Yeah.

1980
01:24:12,880 --> 01:24:15,670
AUDIENCE: And I had a
lot of personal things

1981
01:24:15,670 --> 01:24:17,710
I was struggling
with that I thought

1982
01:24:17,710 --> 01:24:21,550
I had to leave behind me when I
would come into the workplace.

1983
01:24:21,550 --> 01:24:25,030
And it almost became this
really overbearing burden

1984
01:24:25,030 --> 01:24:28,690
of, I had to keep this
personal side of me a secret

1985
01:24:28,690 --> 01:24:32,260
and I could only have this
academic side of me in public.

1986
01:24:32,260 --> 01:24:36,910
So having a mentor who is very
open about her own struggles,

1987
01:24:36,910 --> 01:24:40,300
and who's willing to share
more about her personal life,

1988
01:24:40,300 --> 01:24:43,210
made me feel more comfortable
bringing my full self

1989
01:24:43,210 --> 01:24:45,370
to the table when I am in lab.

1990
01:24:45,370 --> 01:24:47,920
So that's definitely
had a big impact on me.

1991
01:24:47,920 --> 01:24:48,760
AUDIENCE: Yeah.

1992
01:24:48,760 --> 01:24:51,790
I feel that the
media is gradually

1993
01:24:51,790 --> 01:24:54,580
changing from showing the
typical nerdy scientist

1994
01:24:54,580 --> 01:24:56,357
into showing that,
hey, scientists also

1995
01:24:56,357 --> 01:24:57,190
have personal lives.

1996
01:24:57,190 --> 01:24:57,620
AUDIENCE: Yeah.

1997
01:24:57,620 --> 01:24:58,630
That they are people.

1998
01:24:58,630 --> 01:24:58,830
AUDIENCE: Yeah.

1999
01:24:58,830 --> 01:24:59,090
AUDIENCE: Yeah.

2000
01:24:59,090 --> 01:24:59,350
AUDIENCE: Yeah, yeah.

2001
01:24:59,350 --> 01:25:00,840
AUDIENCE: Yeah, I
really like that.

2002
01:25:00,840 --> 01:25:01,695
So yeah.

2003
01:25:01,695 --> 01:25:02,320
AUDIENCE: Cool.

2004
01:25:02,320 --> 01:25:03,440
AUDIENCE: Cool, cool.

2005
01:25:03,440 --> 01:25:05,023
CATHY L. DRENNAN:
So I'll just briefly

2006
01:25:05,023 --> 01:25:06,910
tell you one story
for me, which,

2007
01:25:06,910 --> 01:25:09,460
if you don't feel comfortable
sharing with your students,

2008
01:25:09,460 --> 01:25:11,200
you can share my stories.

2009
01:25:11,200 --> 01:25:11,920
That's fine.

2010
01:25:11,920 --> 01:25:14,360
I have a long list.

2011
01:25:14,360 --> 01:25:17,740
So one thing that you can tell
people which sometimes helps

2012
01:25:17,740 --> 01:25:20,740
is that professor
Drennan is dyslexic.

2013
01:25:20,740 --> 01:25:24,520
So I was diagnosed
in first grade.

2014
01:25:24,520 --> 01:25:28,050
I was a very smart
and articulate kid,

2015
01:25:28,050 --> 01:25:30,820
and I liked talking to adults.

2016
01:25:30,820 --> 01:25:33,123
And so, they assumed
I was sort of smart,

2017
01:25:33,123 --> 01:25:34,540
but then I couldn't
learn to read.

2018
01:25:34,540 --> 01:25:38,200
So I was in the upper group, and
then the somewhat lower group,

2019
01:25:38,200 --> 01:25:42,400
and lower group, and the lowest
group, and the remedial group.

2020
01:25:42,400 --> 01:25:45,070
And it took me to sixth grade
until I learned how to read--

2021
01:25:45,070 --> 01:25:46,540
second time through sixth grade.

2022
01:25:46,540 --> 01:25:49,100
I had to do that twice.

2023
01:25:49,100 --> 01:25:52,570
And so, I was told I would
never be able to graduate

2024
01:25:52,570 --> 01:25:53,492
from high school.

2025
01:25:53,492 --> 01:25:55,450
And so, when I graduated
from high school, then

2026
01:25:55,450 --> 01:25:56,170
I went to college.

2027
01:25:56,170 --> 01:25:57,337
And then I was like, ha, ha!

2028
01:25:57,337 --> 01:25:57,940
Look at me!

2029
01:25:57,940 --> 01:25:59,770
And then I went to grad
school and then I kept going.

2030
01:25:59,770 --> 01:26:00,353
It's like, oh!

2031
01:26:00,353 --> 01:26:01,570
Faculty position at MIT!

2032
01:26:01,570 --> 01:26:03,220
Let's go!

2033
01:26:03,220 --> 01:26:08,643
And I just needed to prove
that I could do these things,

2034
01:26:08,643 --> 01:26:09,310
but it was hard.

2035
01:26:09,310 --> 01:26:16,390
And there are many really
embarrassing dyslexic mistakes

2036
01:26:16,390 --> 01:26:18,460
that I have made
over the years too.

2037
01:26:18,460 --> 01:26:19,960
And when I first
started here, I was

2038
01:26:19,960 --> 01:26:23,140
very nervous about anybody
knowing that I was dyslexic,

2039
01:26:23,140 --> 01:26:25,310
and I have gotten beyond that.

2040
01:26:25,310 --> 01:26:28,500
So I've now been
videotaped by a whole bunch

2041
01:26:28,500 --> 01:26:30,750
of the dyslexic
associations, and done

2042
01:26:30,750 --> 01:26:33,340
all sorts of different things
where I share my stories.

2043
01:26:33,340 --> 01:26:36,660
So if you don't want to share a
story about something from you,

2044
01:26:36,660 --> 01:26:39,360
you can share my stories,
even if they're my students.

2045
01:26:39,360 --> 01:26:43,530
I am cool with everybody
knowing that I am dyslexic.

2046
01:26:43,530 --> 01:26:48,210
And if you're my TA, and
I write words that are not

2047
01:26:48,210 --> 01:26:50,340
words on the board, or
switch numbers around,

2048
01:26:50,340 --> 01:26:52,530
just say, "Professor
Drennan, other way,"

2049
01:26:52,530 --> 01:26:53,560
or something like that.

2050
01:26:53,560 --> 01:26:54,460
That's cool.

2051
01:26:54,460 --> 01:26:56,500
So feel free to
share these stories.

2052
01:26:56,500 --> 01:26:58,920
And I think it's so important
that students know there's

2053
01:26:58,920 --> 01:27:00,750
more than one path to success.

2054
01:27:00,750 --> 01:27:02,320
And that not
everyone is the same,

2055
01:27:02,320 --> 01:27:04,237
and people may struggle
with different things.

2056
01:27:04,237 --> 01:27:07,290
They may have different
insecurities, but it's all OK.

2057
01:27:07,290 --> 01:27:11,310
And those struggles
often make us who we are,

2058
01:27:11,310 --> 01:27:14,100
and that science doesn't work
perfectly the first time.

2059
01:27:14,100 --> 01:27:17,340
We have to get in there and
just keep working at it.

2060
01:27:17,340 --> 01:27:19,230
And the struggles we
face in our own life

2061
01:27:19,230 --> 01:27:22,290
is often just a really
good motivating factor

2062
01:27:22,290 --> 01:27:25,080
for doing some really
great research.

2063
01:27:25,080 --> 01:27:25,580
All right.

2064
01:27:25,580 --> 01:27:29,750
So again, you're very
welcome to share my stories.

2065
01:27:29,750 --> 01:27:32,910
I have extra ones, if
you want to hear times

2066
01:27:32,910 --> 01:27:35,420
that I felt insecure.

2067
01:27:35,420 --> 01:27:39,050
But I heard a lot of
discussion, so maybe people

2068
01:27:39,050 --> 01:27:42,590
feel like they are willing to
share a story with us right

2069
01:27:42,590 --> 01:27:44,485
now?

2070
01:27:44,485 --> 01:27:45,500
Yeah?

2071
01:27:45,500 --> 01:27:47,780
AUDIENCE: Going off what we
were discussing about how

2072
01:27:47,780 --> 01:27:50,660
in science, so much of what
we hear, or what's told to us,

2073
01:27:50,660 --> 01:27:53,390
is all successes.

2074
01:27:53,390 --> 01:27:57,080
And also knowing that a lot of
those failures, at least for me

2075
01:27:57,080 --> 01:27:58,905
personally, they kind
of come in waves.

2076
01:27:58,905 --> 01:28:00,530
There's not just one
failure at a time.

2077
01:28:00,530 --> 01:28:01,070
It's a lot.

2078
01:28:01,070 --> 01:28:01,730
[SOFT LAUGHTER]

2079
01:28:01,730 --> 01:28:06,980
And it really wore on me at the
time, but what I think really

2080
01:28:06,980 --> 01:28:11,150
helped me was my own mentor
telling me that she struggled

2081
01:28:11,150 --> 01:28:12,050
through her PhD.

2082
01:28:12,050 --> 01:28:13,490
It took a long time.

2083
01:28:13,490 --> 01:28:15,200
It was a slog.

2084
01:28:15,200 --> 01:28:17,210
It just reminded me
that it's not always

2085
01:28:17,210 --> 01:28:19,280
a straight line to
get to the finish.

2086
01:28:19,280 --> 01:28:24,470
And more importantly, it's
that things will get better.

2087
01:28:24,470 --> 01:28:25,760
It'll all work out.

2088
01:28:25,760 --> 01:28:27,030
Everything's going to be OK.

2089
01:28:27,030 --> 01:28:31,522
And I think that really helped
me continuing on to my PhD.

2090
01:28:31,522 --> 01:28:32,480
CATHY L. DRENNAN: Yeah.

2091
01:28:32,480 --> 01:28:33,830
Yeah, that's wonderful.

2092
01:28:33,830 --> 01:28:34,370
Yeah.

2093
01:28:34,370 --> 01:28:37,380
And I often think of the waves.

2094
01:28:37,380 --> 01:28:40,800
And it's like things are
going really, really well,

2095
01:28:40,800 --> 01:28:43,790
and then it slows down, and
then things are not working.

2096
01:28:43,790 --> 01:28:47,030
And if the amplitude was up
and down and this, it's like,

2097
01:28:47,030 --> 01:28:47,810
I'll be OK.

2098
01:28:47,810 --> 01:28:50,985
But often it's up and then down,
and then down, and then down,

2099
01:28:50,985 --> 01:28:53,060
and then down, and then down.

2100
01:28:53,060 --> 01:28:55,275
And you're like, in other
people their amplitudes are

2101
01:28:55,275 --> 01:28:56,150
different in the lab.

2102
01:28:56,150 --> 01:28:58,025
And it's like, that
person has stuff working.

2103
01:28:58,025 --> 01:28:59,540
This person has stuff working.

2104
01:28:59,540 --> 01:29:01,700
What is wrong?

2105
01:29:01,700 --> 01:29:03,613
But the thing is
that over time, it's

2106
01:29:03,613 --> 01:29:05,780
like when it's down like
this, then when it goes up,

2107
01:29:05,780 --> 01:29:08,420
it stays up longer also.

2108
01:29:08,420 --> 01:29:10,790
So it's like getting
you there again.

2109
01:29:10,790 --> 01:29:14,972
So yeah, that's why these
stories can be pretty powerful.

2110
01:29:14,972 --> 01:29:16,680
Anybody else have one
they want to share?

2111
01:29:16,680 --> 01:29:17,180
Yeah?

2112
01:29:17,180 --> 01:29:20,630
AUDIENCE: I think why
it's been difficult for me

2113
01:29:20,630 --> 01:29:22,700
during grad school,
especially in the beginning,

2114
01:29:22,700 --> 01:29:26,550
was that I was dealing with
some personal struggles

2115
01:29:26,550 --> 01:29:28,700
from before grad
school and during.

2116
01:29:28,700 --> 01:29:31,280
And I kind of felt
like I had this burden

2117
01:29:31,280 --> 01:29:34,430
of keeping it a secret, because
I didn't want to come off

2118
01:29:34,430 --> 01:29:35,930
as overly emotional.

2119
01:29:35,930 --> 01:29:39,320
Or that because I was dealing
with these personal struggles,

2120
01:29:39,320 --> 01:29:42,350
that I wasn't taking
my sciences seriously.

2121
01:29:42,350 --> 01:29:44,510
So having a mentor
that opened up

2122
01:29:44,510 --> 01:29:47,150
about how she had also
had personal struggles

2123
01:29:47,150 --> 01:29:49,880
and how she still was
able to be successful,

2124
01:29:49,880 --> 01:29:53,330
was really meaningful
to me, to feel

2125
01:29:53,330 --> 01:29:56,122
like I could be both a
person and a scientist.

2126
01:29:56,122 --> 01:29:57,330
CATHY L. DRENNAN: Yeah, yeah.

2127
01:29:57,330 --> 01:29:58,580
Yeah, yeah.

2128
01:29:58,580 --> 01:30:00,330
It is one package, right?

2129
01:30:00,330 --> 01:30:01,680
Yeah.

2130
01:30:01,680 --> 01:30:03,210
We're all a package
of these things.

2131
01:30:03,210 --> 01:30:04,590
So yeah, that's wonderful.

2132
01:30:04,590 --> 01:30:08,460
And I think that sometimes
it just looks to people

2133
01:30:08,460 --> 01:30:10,830
as if everything
is going so well,

2134
01:30:10,830 --> 01:30:13,170
and they just don't see
the inside struggles.

2135
01:30:13,170 --> 01:30:14,880
And you don't really
know, necessarily,

2136
01:30:14,880 --> 01:30:15,960
what else is going on.

2137
01:30:15,960 --> 01:30:19,140
And so, when you're comfortable
sharing these things,

2138
01:30:19,140 --> 01:30:20,348
it can be so powerful.

2139
01:30:20,348 --> 01:30:22,890
And I feel like sometimes when
people are getting through it,

2140
01:30:22,890 --> 01:30:25,590
it's like, now you have to
be a professor because you've

2141
01:30:25,590 --> 01:30:28,330
got to share these
stories with other people.

2142
01:30:28,330 --> 01:30:30,882
And that figuring out how to
deal with the struggle, I mean,

2143
01:30:30,882 --> 01:30:31,840
that's what it's about.

2144
01:30:31,840 --> 01:30:34,470
It's not about doing everything
perfectly the first time.

2145
01:30:34,470 --> 01:30:36,630
It's how to deal with
failure, get yourself

2146
01:30:36,630 --> 01:30:38,735
up, get through those times.

2147
01:30:38,735 --> 01:30:40,860
And that's what gives you
the strength to really go

2148
01:30:40,860 --> 01:30:42,820
after doing hard stuff.

2149
01:30:42,820 --> 01:30:43,320
All right.

2150
01:30:43,320 --> 01:30:47,990
Are there any other ones?

2151
01:30:47,990 --> 01:30:48,580
OK.

2152
01:30:48,580 --> 01:30:49,080
All right.

2153
01:30:49,080 --> 01:30:50,740
Let's move on.

2154
01:30:50,740 --> 01:30:52,450
The bad news.

2155
01:30:52,450 --> 01:30:53,473
We all have stereotypes.

2156
01:30:53,473 --> 01:30:55,390
I think all of you are
sitting there thinking,

2157
01:30:55,390 --> 01:30:59,130
man, let me just count all the
different stereotypes I have.

2158
01:30:59,130 --> 01:31:00,130
We all have stereotypes.

2159
01:31:00,130 --> 01:31:01,970
We all have unconscious bias.

2160
01:31:01,970 --> 01:31:05,020
Now, maybe some of you will
be like, oh, wait a minute.

2161
01:31:05,020 --> 01:31:06,490
Why did I react that way?

2162
01:31:06,490 --> 01:31:07,390
Oh, man!

2163
01:31:07,390 --> 01:31:10,360
There's another
stereotype I have.

2164
01:31:10,360 --> 01:31:14,410
We all can be victims of
stereotype threats or imposter

2165
01:31:14,410 --> 01:31:15,400
syndrome.

2166
01:31:15,400 --> 01:31:17,650
Stereotype threat can
be pretty harmful.

2167
01:31:17,650 --> 01:31:20,110
It can lead to
underperformance and a feeling

2168
01:31:20,110 --> 01:31:21,820
of being judged unfairly.

2169
01:31:21,820 --> 01:31:23,410
But there's good news.

2170
01:31:23,410 --> 01:31:25,750
You create an environment
of trust in your classroom

2171
01:31:25,750 --> 01:31:28,540
or in your lab,
give wise criticism,

2172
01:31:28,540 --> 01:31:32,410
and that can really mediate the
negative effects of stereotype.

2173
01:31:32,410 --> 01:31:34,690
If you're part of a
team doing something,

2174
01:31:34,690 --> 01:31:37,720
if someone believes that someone
else believes in them-- they're

2175
01:31:37,720 --> 01:31:40,452
not sure they can do it,
but my team believes in me,

2176
01:31:40,452 --> 01:31:42,160
therefore, I can't do
it, because my team

2177
01:31:42,160 --> 01:31:44,080
is pretty smart.

2178
01:31:44,080 --> 01:31:45,520
These are all ways--

2179
01:31:45,520 --> 01:31:49,220
and we have to help everyone
reach their full potential.

2180
01:31:49,220 --> 01:31:51,490
The students you'll be
working with this semester

2181
01:31:51,490 --> 01:31:54,160
are crazy, smart,
and cool people.

2182
01:31:54,160 --> 01:31:56,410
And some of them
may be insecure,

2183
01:31:56,410 --> 01:31:59,450
and you can really help them
get to their full potential.

2184
01:31:59,450 --> 01:32:03,410
And it's an amazing opportunity
to teach smart people.

2185
01:32:03,410 --> 01:32:05,260
So I hope you have
a wonderful time,

2186
01:32:05,260 --> 01:32:08,770
and I hope that those horses
that are dressed like zebras

2187
01:32:08,770 --> 01:32:10,960
in your classroom are
feeling very zebra-ish

2188
01:32:10,960 --> 01:32:13,210
by the end of the semester.

2189
01:32:13,210 --> 01:32:14,960
So thank you very much
for your attention.

2190
01:32:14,960 --> 01:32:17,240
I loved the stories
you're shared today,

2191
01:32:17,240 --> 01:32:20,080
and have a wonderful semester.

2192
01:32:20,080 --> 01:32:22,830
[APPLAUSE]