4. OVERALL PARTICIPATION
Percentage of computing occupations held by women has been declining since 1991
(NCWIT Women in Tech Facts Report 2016)
5. FEMALE RETENTION
50% of women in STEM left jobs in their 12yr mark, while 80% of non-STEM
professionals remains. (NCWIT Women in Tech Facts Report 2016)
15. STEREOTYPE THREAT
THE EXPERIENCE OF ANXIETY IN A SITUATION IN WHICH A
PERSON HAS THE POTENTIAL TO CONFIRM A NEGATIVE
STEREOTYPE ABOUT HIS OR HER SOCIAL GROUP
17. FROM INDIVIDUALS
• What glasses are YOU wearing?
• The Growth Mindset
One’s intelligence and ability is not fixed, but can change and grow
incrementally, with practice and exercise.
• Expand Your Professional Networks
18. FROM COMMUNITIES
• Foster Sense of Belonging
e.g. avoid homogenous interview team, attention called to gender during application,
create inclusive physical environment of office
• Reattribution Training; “It’s okay. We’ve been there”
e.g. mentoring, buddy system, networking, team building
• Increase the visibility and representation of people from minority
groups in a field
• And again..the Growth Mindset
19. LOOKING ON THE BRIGHT SIDE
• 56% of Women in Tech leave their jobs at mid-level position.
- however, 22% of these women do so to start their own business
• 85% of Women in Tech say that they love their job.
• Young women today are 33 percent more likely to study computer
science compared with women born before 1983
24. RESOURCES
• STEREOTYPE THREAT: AN OVERVIEW EXCERPTS AND ADAPTATIONS FROM REDUCING
STEREOTYPE THREAT.ORG
https://diversity.arizona.edu/sites/default/files/stereotype_threat_overview.pdf
• National Center for Women & Information Technology report
https://www.ncwit.org/sites/default/files/resources/womenintech_facts_fullreport_05132016.
pdf
• Empiriacally validated strategies to reduce stereotype threat
https://ed.stanford.edu/sites/default/files/interventionshandout.pdf
• Addressing Stereotype Threat is Critical to Diversity and Inclusion in Organizational
Psychology https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4718987/
• Implicit Association Test http://implicit.harvard.edu/implicit/
Editor's Notes
Looking at my session topic, some of you might think, are women in tech really, unicorns?
Now before I move on, when I say unicorns, I’m not talking about these kind of unicorns.
What I’m talking about is, that the ratio of men and women working professionally in technology is disproportionate.
According to the National Centre of Women & Information Technology report in 2016, since 1991 the percentage of computing occupations held by women has been declining continuously.
While around 50% of professional occupations in the US is held by women, if we double down to tech section, the ratio decreases to 25%.
So what happend in the 1990s?
The first computers were beginning to trickle out into the public for general use in the 1980s. The Commodore 64 and the Apple Macintosh were both released in the early 1980s, bringing computers into the home. Computers weren’t cheap. A Commodore 64 costs roughly $1500 dollars in today’s money and an Apple Macintosh would set you back a cool $5,763 today.
As time went on, computer science professors began to assume that all of their students had a basic understanding of computers coming into introductory classes. For boys who grew up with computers, this was not a problem. But for girls, it meant less and less of them got over that initial hurdle.
And so, as the graph shows, the number of women in computer science decreased into the state we find ourselves in today.
https://jaxenter.com/women-in-computer-science-majors-133646.html
During the 1980s, as tech products were advertised in the media, computer science became popular with consumers. DuBow says this was also when computing started to be viewed as a male profession and a popular choice of majors among college students. “Advertisements depicting women not knowing how to use technology became ubiquitous, stereotypes of the male nerd or hacker emerged, gaming aimed at men also started gaining prominence, and these were reinforced by the growth of popular computing companies run by ‘male geniuses,’” DuBow says.
And this stereotype of there not being women in computer science was reinforced on more than one level. Fisher believes that when the home computer was introduced, it was marketed for use by boys instead of both genders. “As a result, boys were encouraged to spend a lot of time playing with computers and exploring how they worked, while girls were not.” As a result, Fisher says boys gained much more exposure to computers, and some emerged as gurus in computers and programming.
https://www.goodcall.com/news/women-in-computer-science-09821
(show Sinclair ZX spectrum ad)
Research shows that six women programmed the first electronic computer — the pioneering Electrical Numerical Integrator and Computer (ENIAC) — during WWII but weren’t given credit for their work, even identified in photos as “refrigerator ladies,” models used to make products appear more alluring. Their groundbreaking work went unrecognized and unlauded for years.
What about retention rate?
This graphs shows a comparison between female STEM and non-STEM professionals. In the earlier career, there is a drastic gap between retention rate of STEM and non-STEM professionals. If you look at the 12yr mark, 50% of women in STEM left jobs, while 80% of non-STEM professionals remains.
https://www.ncwit.org/sites/default/files/resources/womenintech_facts_fullreport_05132016.pdf
So what can I say. The tech world, is still a man’s world.
According to diversity reports of major tech firms, ratio of women in total workforce ranges between 27% - 47%, with the percentage dropping much lower when it comes to actual tech jobs.
In terms of leadership positions, the ratio roams around 30% in average.
https://www.statista.com/chart/4467/female-employees-at-tech-companies/
Looking at my session topic, some of you might think, are women in tech really, unicorns?
Sharing my story -
I’d like to share a little bit about myself.
What you are seeing here are a number of ajjummas, what we call middle aged women in Korea, equivalent to ‘Aunties’, in a temple praying for their sons and daughters. People in this photo are here for a particular prayer, on a particular day.
University entrance exam. As extreme it may look like, university entrance exam is a huge deal in Korea, considered the culmination of 3yrs of highschool and 3yrs of middle school combined, and a touchstone that will determine whether your adulthood will be successful or not.
What I’m trying to say here is not that my mother was part of desperate prayers. It’s the fact after taking the exam, students will choose their major at university before freshmen years. And because depending on what major you want to take the subjects of the exam will be different, this exam is actually prepared for in quite an early age. In fact, in the age of 13, which is right after primary school graduation.
I also took this exam. There are two main streams of the exam; science, or literature. Taking the science exam helps you choose majors in computer science, biology, chemistry, so on and so forth, while the latter allows you to apply for majors like business, marketing, language, psychology etc.
Multiple studies have shown that if you tell groups of men and women that scores on a math test will show a gender difference before they take it, their scores will show the difference. This is what we call an ‘achievement gap’.
But if you tell them the results are the same, the difference often goes away.
You can even see the difference from whether you ask demographic questions at the beginning of the test, and at the end. If the question asking your gender is at the beginning of the exam, female students scored lower than male students. While when the question was at the end of the exam, there was no prominent difference.
This question applies to other subjects as well. One study did this experiment with Asian-American women. One group wrote about their Asian identity, another group wrote about their identity as a woman, and the last group was the control group.
The result? The group that identified as women had the lowest score, while the group that wrote about their Asian identity had the highest score.
The result? The group that identified as women had the lowest score, while the group that wrote about their Asian identity had the highest score.
Stereotype threat is the experience of anxiety in a situation in which a person has the potential to confirm a negative stereotype about his or her social group (e.g. gender, race)
The term was first used by Steele and Aronson (1995) who tried the experiments with Black and White university students.
Since then, more than 300 research studies have documented that these fears and anxieties reduce feelings of competence and belonging, and can negatively affect performance (see Aronson et al., 1999, and reducingstereotypethreat.org for more information).
Since most people have at least one social identity which is negatively stereotyped, most people are vulnerable to stereotype threat if they encounter a situation in which the stereotype is relevant.
https://www.apa.org/research/action/stereotype
How Stereotype Threat Might Show Up In Technical Workplaces • Reluctance to speak up in team meetings or to take on leadership positions. • Appearing “less confident” in interview settings. • Reduced performance in interviews or other work contexts. • Tendency to discount own performance during reviews and evaluations.
Now, I’m not saying stereotype threats around gender was the one and only reason why fewer women pursuit higher education studies in technology, and more so choose their professions.
However, stereotype threats has been with us for a very long time, and has been identified. Which means, we can figure out ways to remove or at least, remediate it.
First of all, understand YOUR biases. Everyone, in any way, has stereotypes to a certain extent. And that’s not bad. At the end of the day, stereotypes are overgeneralized beliefs about a particular group of people, which could be a positive, negative, or neutral perspective.
However when stereotypical beliefs combines with a certain attitude, such as discomfort, hostility, or fear, it becomes prejudice. And when it is shown as a behavior, that becomes discrimination. So look back, step back, and observe yourself.
Secondly, build a Growth Mindset.
People with growth mindsets are less likely to become discouraged after making mistakes and more likely to view difficult situations as challenges rather than threats. Adopting a growth mindset can benefit everyone, but it might be especially important for those who belong to stereotyped groups.
Lastly, put yourself out there. Expand your network.
Feelings of belonging directly influence people's motivation and satisfaction with a scientific career and can predict whether they stay at an institution.
c.f.) Implicit Association Test
the IAT is used to measure the strength of associations between an attitude object and its valence. IATs measure the relative ease with which people are able to make associations between certain groups of people (e.g., older adults) and the concepts of "good" and "bad.“
Avoid environments that reinforce stereotypes
e.g. homogenous interview team such as having white males,
This discourages sense of belonging, and cause reduction of job engagement, career aspiration, and receptivity to feedback.
Reattribution Training
When facing challenges common in the workplace, employees who attribute hardships to temporary, external factors are more likely to excel in the face of failure than employees who attribute setbacks to internal factors such as ability (Weiner, 1985).
Hence on top of giving sense of belonging to new joiners, reminding them that the things they are going through is something that can be overcome, and was experienced by many of those who joined the community previously.
Consider the following scenario. During the onboarding process, employers can share stories with new employees about others’ experiences when first joining company. For example, highlighting cases where individuals first felt like an outsider, but then developed a sense of community after joining an organization-related club. When a new trainee experiences difficulty learning a new job skill, the trainer can emphasize that other new employees experienced initial trouble but mastered the skill after practice, which will diffuse the negativity of the setback.
The Growth Mindset
Emphasize effort rather than perceived innate abilities.
An entity or fixed mindset reflects beliefs that intelligence is something humans are born with and that the capacity to increase intelligence occurs within innate boundaries. This mindset promotes viewing mistakes and challenges as evidence of low intelligence. In contrast, an incremental or malleable view of intelligence suggests that intelligence is a result of learning and hard work and that anyone can increase their intelligence. In this mindset, mistakes are viewed as an important part of the learning process.
Research with adolescents (Paunesku et al., 2015), girls (Good et al., 2003), and racial minorities (Aronson et al., 2002) struggling with math shows that incremental mindsets predict learning and achievement. Recent work has documented that organizations perceived to have fixed mindsets elicited more stereotype threat among women (Emerson and Murphy, 2015). Organizations perceived to have a growth (incremental) mindset did not elicit threat and women reported greater trust and commitment to the organization and had higher performance (Emerson and Murphy, 2015).
It is important to remember that, more often that not, these biases are not the result of any ill intentions. The goal, therefore, is not to find fault or assign blame; this is not about fixing people, but rather it is about recognizing and interrupting these biases
The good news is, that these kinds of efforts have been made.
Source: https://www.rubiconcentre.ie/8-facts-about-women-in-tech/
https://www.techopedia.com/minding-the-gender-gap-10-facts-about-women-in-tech/2/33715
And the stereotypes, fixations, and entitlements don’t end in gender. It happens an every social identity.
