WEBVTT

00:00:09.980 --> 00:00:12.250 
Stephanie:
Hello, I’m Stephanie Hakulin.

00:00:14.400 --> 00:00:16.400 
Today on STEM Workforce Stories...

00:00:19.660 --> 00:00:21.490 
we’ll meet Alma Schrage,

00:00:23.750 --> 00:00:27.340 
a Naturalist, who will clue
us in to a little-known fact.

00:00:33.380 --> 00:00:37.840
Alma: So, when I was at UC Berkeley
and I was studying bird song...

00:00:42.170 --> 00:00:45.820
Yeah, you may ask how Deaf
people can study bird song.

00:00:50.970 --> 00:00:56.350
Stephanie: We’ll learn this, and a whole
lot more, today - on STEM Workforce Stories.

00:01:09.040 --> 00:01:13.010
Alma: So, my name
is Alma Schrage.

00:01:16.780 --> 00:01:19.350
I'm deaf. I was born deaf.

00:01:22.000 --> 00:01:23.830
My family is all hearing.

00:01:25.360 --> 00:01:26.910
I grew up speaking.

00:01:28.580 --> 00:01:30.760
I grew up in Chicago.

00:01:32.720 --> 00:01:35.040
I went to California for undergrad.

00:01:36.990 --> 00:01:40.850
And I worked in California
for about four years.

00:01:42.770 --> 00:01:46.400
After that, I decided
to move back to Illinois.

00:01:46.420 --> 00:01:50.020
Specifically, I wanted
to go to grad school here.

00:01:50.050 --> 00:01:54.530
And because I have family
here, I still felt connected.

00:01:54.550 --> 00:02:00.150
I identify as deaf, and
I identify as disabled.

00:02:01.330 --> 00:02:03.080
I also have ADHD...

00:02:05.080 --> 00:02:09.100
Lily: So then how did you learn to sign?
When did you start learning ASL?

00:02:13.400 --> 00:02:16.260
Alma: When I moved to
California for undergrad.

00:02:16.280 --> 00:02:19.610
I went to University
of California, Berkeley.

00:02:23.080 --> 00:02:27.180
At that time they didn't
offer any ASL classes.

00:02:27.790 --> 00:02:33.020
But in my third year there, they
finally started offering ASL,

00:02:33.040 --> 00:02:35.800
so I decided to take
advantage of that.

00:02:38.190 --> 00:02:40.750
Lily: So next I just wanted
to ask, how did you get

00:02:40.780 --> 00:02:43.520
into that Natural History,
doing that surveying?

00:02:48.810 --> 00:02:54.600
Alma: So growing up, I was always
interested in nature, hiking, backpacking.

00:03:01.210 --> 00:03:06.590
Growing up, I knew I wanted to do
either biology, work as a biologist,

00:03:06.610 --> 00:03:09.090
or I wanted to become an artist.

00:03:13.460 --> 00:03:18.960
But the research, I felt, was always
really interesting and easy to do.

00:03:19.590 --> 00:03:21.170
I fell right into it.

00:03:21.200 --> 00:03:26.930
So, I started doing research every
day out in the fields in grad school.

00:03:28.820 --> 00:03:32.710
So I'm working right now
towards my master's in science.

00:03:35.700 --> 00:03:37.690
Focusing on specifically...

00:03:39.820 --> 00:03:41.550
the natural resources...

00:03:47.440 --> 00:03:49.450
and environmental sciences.

00:04:01.910 --> 00:04:03.290
Lily: Where do you currently work?

00:04:06.560 --> 00:04:09.640
Alma: I work for Illinois
Natural History Survey.

00:04:19.580 --> 00:04:22.140
I've been working there
for the past two years.

00:04:23.380 --> 00:04:25.200
I'm also a grad student still.

00:04:27.740 --> 00:04:29.880
Lily: Are you going to
the University of Illinois?

00:04:32.750 --> 00:04:37.710
Alma: Yeah, actually I work and I
study with the University of Illinois.

00:04:40.770 --> 00:04:44.910
Lily: What does your normal workday look
like? Throughout the day what are your tasks?

00:04:47.130 --> 00:04:54.420
Alma: So during the winter time, I work
primarily on my laptop doing coding and analysis.

00:04:54.450 --> 00:04:58.160
During the spring and the summer,
things are a little bit different.

00:05:00.480 --> 00:05:03.490
I typically do my
field work at that time.

00:05:03.510 --> 00:05:10.250
So specifically, I do biological research
outside when I'm doing my fieldwork.

00:05:12.090 --> 00:05:13.870
I study bumblebees.

00:05:18.220 --> 00:05:21.220
My sign for bumblebees is this.

00:05:21.250 --> 00:05:26.370
In the past I studied birds, but
I have moved on to study bees.

00:05:32.910 --> 00:05:36.270
Lily: What type of equipment do you use when
you're doing your research on bumblebees?

00:05:39.000 --> 00:05:44.590
Alma: When I'm doing my fieldwork, I use a
variety of different equipment like a net

00:05:44.610 --> 00:05:46.520
in order to catch the bumblebee.

00:05:50.650 --> 00:05:55.770
Once I catch the bumblebees, I record a
lot of different data in regards to them.

00:05:59.250 --> 00:06:03.370
So this program that
I use is called GIS.

00:06:03.390 --> 00:06:04.650
It's for mapping.

00:06:08.320 --> 00:06:14.100
So right now I'm showing four different
fields on the satellite image.

00:06:14.120 --> 00:06:15.760
They're my field sites.

00:06:15.780 --> 00:06:18.570
This is where I
look for bumblebees.

00:06:22.160 --> 00:06:25.090
So I'm looking for a
specific bumblebee.

00:06:25.120 --> 00:06:28.070
It's called the rusty
patched bumblebee.

00:06:37.770 --> 00:06:42.340
So when I was at UC Berkeley
and I was studying bird song...

00:06:46.470 --> 00:06:50.600
Yeah, you may ask how Deaf
people can study bird song.

00:06:52.640 --> 00:06:57.390
So, when you research bird songs
you can get something like that.

00:07:00.760 --> 00:07:06.080
So what I do, right, I record a
bird song and I look at my laptop,

00:07:06.100 --> 00:07:08.900
and it shows me the
bird song in frequency.

00:07:16.850 --> 00:07:20.060
It's the same approach
as hearing researchers do.

00:07:22.510 --> 00:07:25.240
When they're studying
birds and bird song,

00:07:25.270 --> 00:07:28.440
they look at these frequency
patterns on their computer.

00:07:30.190 --> 00:07:31.790
I use my smartphone.

00:07:32.390 --> 00:07:36.010
It has an app that shows
the frequency of the Birdsong.

00:07:37.460 --> 00:07:43.460
So when I'm out and about in my field and
I see a bird, I notice it may be singing.

00:07:44.950 --> 00:07:50.810
So I pull out my app and I can record
that song and then later identify it.

00:07:52.280 --> 00:07:54.920
So this technology
records sound.

00:07:58.390 --> 00:08:01.050
So I'll put it next to
flowers, for example,

00:08:01.070 --> 00:08:05.530 
and record what bumblebees are
coming to that specific flower.

00:08:13.290 --> 00:08:19.350 
I will then eventually build a frequency
chart similar to the Birdsong frequency chart.

00:08:23.400 --> 00:08:27.630 
I basically traced and
back lit those sound waves.

00:08:27.650 --> 00:08:31.360 
And then I projected those
sound waves onto a wall.

00:08:43.070 --> 00:08:46.240 
I had a lot of fun
with sound in college.

00:08:49.530 --> 00:08:53.810 
In Illinois, we have roughly 10
different species of bumblebees.

00:08:57.280 --> 00:09:01.000 
And we have about
500 native bees.

00:09:04.970 --> 00:09:10.380 
But in general America, we have
roughly 4,000 species of bees.

00:09:15.000 --> 00:09:16.710 
Lily: Thank you
for sharing that.

00:09:16.730 --> 00:09:19.270 
That's actually a
very fun fact for me,

00:09:19.270 --> 00:09:23.880 
something for me to learn in relation
to bees, bumblebees, native bees.

00:09:23.910 --> 00:09:25.030 
I had no idea.

00:09:26.460 --> 00:09:28.700 
I'm curious what
sparked your interest?

00:09:32.280 --> 00:09:35.500 
Alma: Yeah, it's
definitely cool research.

00:09:36.340 --> 00:09:39.420 
That's an interesting
story how I got into it.

00:09:40.100 --> 00:09:44.080 
When I was growing up, I
was obsessed with birds.

00:09:44.100 --> 00:09:47.810 
I loved watching
them, observing them.

00:09:48.270 --> 00:09:52.060 
I knew that I wanted to work
with birds when I got older.

00:09:52.740 --> 00:09:57.110 
So when I went to my undergrad,
I started studying birds.

00:09:57.850 --> 00:10:01.180 
And I know that my
first job after graduation

00:10:01.210 --> 00:10:06.270 
included fieldwork where I
studied both birds and bumblebees,

00:10:06.970 --> 00:10:09.910 
so that's how I got
exposed to bumblebees.

00:10:12.570 --> 00:10:15.960 
I really liked working with
this group of researchers.

00:10:15.990 --> 00:10:19.370 
It was a non-for-profit
organization that I joined.

00:10:20.170 --> 00:10:25.350 
So I went and worked with them
every year for about four years.

00:10:30.810 --> 00:10:34.470 
Lily: I'm curious, what has been your
personal and professional challenges

00:10:34.490 --> 00:10:37.010 
that you faced while you
were attending UC Berkeley?

00:10:39.420 --> 00:10:45.060 
Alma: I had a mainstream education, meaning
I was the only deaf person in my school.

00:10:46.280 --> 00:10:51.390 
So my parents were actually the first to
go to college in their respective families.

00:10:55.010 --> 00:10:59.230 
The two of them really realized
how valuable education is

00:10:59.250 --> 00:11:02.280 
and they wanted me
to also go to college.

00:11:03.630 --> 00:11:07.790 
Growing up they would always tell
me that's what I was going to do

00:11:07.820 --> 00:11:09.820 
after I graduated high school.

00:11:12.710 --> 00:11:17.150 
I went to college undergrad
at UC Berkeley in California

00:11:22.330 --> 00:11:26.130 
and I studied two
fields, biology and art.

00:11:27.640 --> 00:11:31.550 
I eventually graduated and
with a passion for biology,

00:11:33.980 --> 00:11:37.670 
I decided to work at that
job that included fieldwork.

00:11:41.210 --> 00:11:48.450 
Fieldwork is critical for biologist, and I
definitely wanted to do more of that kind of work

00:11:48.480 --> 00:11:52.100 
so I stayed there
for about four years.

00:11:53.850 --> 00:11:59.670 
When I applied to Berkeley, I had art skills
and writing skills that were very strong.

00:12:00.100 --> 00:12:03.240 
Berkeley was very difficult
though to get through.

00:12:03.780 --> 00:12:08.250 
But because of my experience growing
up in the mainstream program,

00:12:08.280 --> 00:12:10.690 
I wasn't too culture shocked.

00:12:10.710 --> 00:12:16.630 
I had experienced feeling alone,
isolated and not being understood.

00:12:16.650 --> 00:12:19.690 
That was the normal
day to day for me.

00:12:23.240 --> 00:12:26.620 
Lily: How has school made accommodations
with you currently with the pandemic?

00:12:32.100 --> 00:12:34.900 
Alma: That's a really
interesting question.

00:12:35.470 --> 00:12:38.830 
First of all, most of my
work nowadays is remote.

00:12:42.340 --> 00:12:45.850 
When I am doing fieldwork
though, it is quite easy

00:12:45.880 --> 00:12:48.820 
to socially distance from
my research partners.

00:12:51.010 --> 00:12:55.620 
So I'm pretty fortunate that my work
wasn't too affected by the pandemic.

00:12:59.540 --> 00:13:04.560 
Now in terms of communication,
it's been hard going.

00:13:04.580 --> 00:13:08.600 
I grew up lip reading, so
masks are a real barrier.

00:13:09.070 --> 00:13:12.590 
I can't understand
anything that anyone says.

00:13:13.250 --> 00:13:19.890 
So I decided to start using a smartphone
app in order to ask people to speak into it

00:13:19.910 --> 00:13:21.860 
so I could read
the transcription.

00:13:23.350 --> 00:13:28.630 
If I went to a grocery store, I went
to the pharmacy, I would use this app.

00:13:30.070 --> 00:13:36.280 
It's interesting when the pandemic
occurred, I think deaf people in general

00:13:36.310 --> 00:13:39.160 
experience a lot of
hardships and barriers.

00:13:40.750 --> 00:13:46.290 
People experienced quite a lot of
fatigue, the deaf people specifically,

00:13:46.320 --> 00:13:50.290 
which is pretty normal as a deaf
person going through the world.

00:13:50.320 --> 00:13:54.780 
But when the pandemic occurred,
I think people that could hear

00:13:54.800 --> 00:14:00.150 
actually started understanding how difficult
it is to communicate with a mask on.

00:14:07.000 --> 00:14:09.370 
Lily: What advice would
you say to your younger self?

00:14:12.710 --> 00:14:15.860 
I'm thinking of middle school
Alma, high school Alma,

00:14:15.890 --> 00:14:17.620 
or just high school students,

00:14:21.240 --> 00:14:22.780 
middle school
students that are deaf

00:14:22.810 --> 00:14:25.710 
and maybe they are motivated
to get into the stem field.

00:14:28.830 --> 00:14:31.730 
They're into engineering,
technology, biology.

00:14:32.860 --> 00:14:34.250 
What advice would you give them?

00:14:37.580 --> 00:14:41.870 
Alma: Worry less about your
fluency in ASL and English.

00:14:46.670 --> 00:14:53.420 
I remember I grew up speaking, but I always
worried about my fluency and sign language

00:14:53.440 --> 00:14:57.160 
and people would have to be
patient with me while learning.

00:15:00.920 --> 00:15:06.060 
Oh, be visible with your
disability and your deafness.

00:15:06.090 --> 00:15:07.770 
Don't be ashamed of it.

00:15:10.140 --> 00:15:12.620 
Don't feel like it is a stigma.

00:15:12.640 --> 00:15:15.920 
Work on de-stigmatizing
it for yourself.

00:15:15.950 --> 00:15:19.980 
No one's going to give you the
things you need if you don't ask.

00:15:21.960 --> 00:15:25.350 
And don't be afraid to meet people
that are different from you.

00:15:29.390 --> 00:15:30.520 
Yeah.

00:15:30.540 --> 00:15:34.410 
And be prepared to
make a lot of mistakes.

00:15:34.430 --> 00:15:37.510 
Don't be afraid to
make mistakes either.

00:15:37.530 --> 00:15:40.090 
You learn from your mistakes.

00:15:40.110 --> 00:15:43.420 
The mistake itself isn't
equally as important.

00:15:45.220 --> 00:15:48.980 
Where I am today, it took
a lot of patience and grit...

00:15:50.380 --> 00:15:52.020 
and I'm stronger for it.

00:15:53.430 --> 00:15:55.520 
And now I feel confident.

00:15:58.660 --> 00:16:00.550 
So good luck.

00:16:02.860 --> 00:16:07.120 
Lily: So many of the things that
you've said they ring very true to me.

00:16:07.140 --> 00:16:11.390 
Self-advocacy, stubbornness, the 'I
can do it' attitude, self-confidence,

00:16:14.840 --> 00:16:19.130 
absolutely these are important things
that you mentioned that you point out.

00:16:20.340 --> 00:16:23.910 
Not giving up, making mistakes
is okay, we learn from them.

00:16:25.140 --> 00:16:28.120 
This is good stuff for those
that maybe want to get into STEM.

00:16:29.910 --> 00:16:30.780 
Good advice.

00:16:30.800 --> 00:16:32.580 
Thank you so much for sharing.

00:16:32.600 --> 00:16:34.800 
I have enjoyed
every second of it.

00:16:38.510 --> 00:16:39.240 
Alma: Thank you.

00:16:45.540 --> 00:16:47.030 
Stephanie: Thank you, Alma.

00:16:47.050 --> 00:16:49.520 
And thank you to everyone
who joined us today.

00:16:50.070 --> 00:16:53.940 
Alma’s career journey is just
one of many amazing stories.

00:16:53.960 --> 00:16:57.400 
You can check out more of them
- on STEM Workforce Stories.

00:17:00.480 --> 00:17:02.160 
I’m Stephanie Hakulin.

00:17:03.660 --> 00:17:05.100 
See you next time.