Hidden Figures: Black Women in the History of Computing

After the first full week of “being a PhD student” Eva and I went to the screening of Hidden Figures in Kirchberg. Because this film is so closely related to my research, I decided to write my first official blogpost about these extra-ordinary women. Now for the first question: what is the film about?

HIDDEN FIGURES is the incredible untold story of Katherine Johnson (Taraji P. Henson), Dorothy Vaughan (Octavia Spencer) and Mary Jackson (Janelle Monáe)—brilliant African-American women working at NASA, who served as the brains behind one of the greatest operations in history: the launch of astronaut John Glenn into orbit, a stunning achievement that restored the nation’s confidence, turned around the Space Race, and galvanized the world. The visionary trio crossed all gender and race lines to inspire generations to dream big.[1]

In order to understand underlying and sometimes clear tensions, four main themes arise: segregation and race, gender and class, American society, and finally the IT culture. Although these themes seem clearly defined, in reality and in the film they often create complex narratives and scenes with hidden messages. Therefore, it does not make sense to discuss each theme separately, but to embrace their intersection.

The exception: education past the eighth grade

In the opening scene we see a meeting of Katherine’s parents with the principal of her school and her teacher, urging them to accept a scholarship and some money for the trip to send her to the West Virginia Collegiate Institute. In a reaction to an insult, Katherine proudly responds, “I was the first Negro female student at West Virginia University Graduate School.” [2] Starting around the turn of the century, “growing numbers of Black women had the opportunity to enter college and the professions,” but “the masses of Black women were still relegated to domestic and menial work.”[3] By 1952 62.4% of degrees from Black colleges went to women.[4] However, “Black women were caught between the two functions they were expected to fulfill: enhancing the material quality of life for their families, and at the same time behaving like housewives.”[5] Another important remark here is the fact that the three protagonists were educated and belonged to the middle-classes.  Even though the film does not mention or show the poorer classes, the women in this film do not represent American Black women in the 1960s, and their position was an exception, rather than the rule.

The East Group vs. the West Group at NASA

When the three women arrive at NASA, they go to the West Group for coloured people where the toilets are not clean, the desks put closely together, and the building blocks exposed. This stands in stark contrast to the architectural details and finishes at the East Group for White people, who get a nicely decorated office and even an armchair in the bathroom. Furthermore, Dorothy has to work a supervisor for the Coloured group, but because they are not assigning a permanent supervisor, she does not get the title or the pay. When she finds out about the construction of an IBM mainframe computer which will eventually take over the function of human “Computers”, she decides to take matters into her own hands. At the library the book on FORTRAN does not belong to the Coloured section, but before the guards can turn her out, she manages to put the book in her purse. She then teaches herself and her division all about working with the machine in order to keep their jobs at NASA, since “somewhere down the line a human being is going to have to hit the buttons.”[6]

In the meantime she gets Mary into a permanent position, and she sends Katherine to the Space Task Computer group. In a group of White men, Katherine faces discrimination on several levels. When she first enters, someone hands her the dustbins assuming she is the cleaning lady. When she puts the bin back down to go to her place, she is stared at as if she is an alien from outer space. Followed by that awkward entry, Katherine faces another challenge, since the bathroom for coloured women is 40 minutes away. After her boss confronts her about her constant absence, she bursts out:

Mr. Harris:            Now where the hell do you go every day?
Katherine:             To the bathroom, sir.
Mr. Harris:            The bathroom! To the damn bathroom! For 40 minutes a day!? What do you do in there!? We are T-minus zero here. I put a lot of faith in you.
Katherine:             There’s no bathroom for me here.
Mr. Harris:            What do you mean there’s no bathroom for you here?
Katherine:             There is no bathroom! There are no colored bathrooms in this building, or any building outside the West Campus. Which is half a mile away! Did you know that? I have to walk to Timbuktu just to relieve myself! And I can’t use one of the handy bikes. Picture that, Mr. Harrison? My uniform. Skirt below the knees and my heels. And simple string of pearls. Well, I don’t own pearls. Lord knows you don’t pay the coloreds enough to afford pearls! And I work like a dog day and night, living of coffee from a pot none of you want to touch! So, excuse me, if I have to go to the restroom a few times a day![7]

When her boss understands that the racism and discrimination is hindering her work, he personally goes to the West Wing to break down the sign reading “colored bathroom”. In front of surprised Black women he shouts “There you have it! No more colored restrooms. No more white restrooms. Just plain old toilets. Go wherever you damn well please. Preferably closer to your desk. At NASA…We all pee the same color!”[8]

Hierarchical Structures: “Fast with rocket ships. Slow with advancement.”

At NASA, there are several hierarchies, starting with Women of Colour being addressed by their first name, whereas White women and men were addressed by their last name. One finding I could not have made manually occurred to me after inserting the text in the Voyant tool.[9] Through textual analysis, it became clear that the most frequent words are Yes (71 instances) and sir (69 instances), often occurring together.[10] When talking to supervisors or other staff higher on the hierarchical ladder, others need to address them in the polite, but almost submissive “Yes Sir.”

Even middle-class and educated women were restricted to female fields, clearly demonstrated twice. Firstly, all Computers were female, and all engineers were male, accompanied by a female secretary. This restriction also shows in the tension between Mr. Stafford and Katherine twice. When Katherine arrives, one of her first jobs is to double-check Mr. Stafford’s math, which he immediately sees as an insult to his work. As a result, he makes her job difficult by crossing out all classified information, and effectively doubling her workload. Later, she has to type his reports and when she adds her name to the list of authors because she contributed, he viciously responds “Computers don’t author reports,” telling her to retype the front page.[11]

[1] “Hidden Figures,” 20th Century Fox, accessed March 16, 2017, http://www.foxmovies.com/movies/hidden-figures.

[2] “Hidden Figures,” 20th Century Fox.

[3] Paula J. Giddings, When and Where I Enter: The Impact of Black Women on Race and Sex in America (Harper Collins, 2009), 73-74.

[4] Giddings, When and Where I Enter, 235.

[5] Giddings, When and Where I Enter, 248.

[6] “Hidden Figures,” 20th Century Fox.

[7] “Hidden Figures,” 20th Century Fox.

[8] “Hidden Figures,” 20th Century Fox.

[9] “Voyant Tools,” Stéfan Sinclair and Geoffrey Rockwell, last modified 2017, voyant-tools.org.

[10] Ibid.

[11] “Hidden Figures,” 20th Century Fox.

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Genderizing Digital Humanities

During our class on Digital Humanities, Politics and Gender we discussed diversity related issues. I would like to start by quoting the definition of diversity:

The condition or quality of being diverse, different, or varied; difference, unlikeness.
(Oxford English Dictionary)

However, diversity can be better understood in terms of intersectionality, which has moved beyond the race-class-gender relationship as described by legal scholar Kimberlé Crenshaw (Crenshaw, 1991), now also including what Roopika Risam specifies as “additional axes of difference including sexuality and ability”. Risam also adds that “as a lens for scholarship [in the digital humanities], intersectionality resists binary logic, encourages complex analysis, and foregrounds difference” (Risam, 2015).

Since I am mostly interested in the gender equity problem, without ignoring the other aspects of intersectionality, I would like to go into detail on society’s ever persistent binary logic when it comes to gender. First, let’s take a look at the psychological and sociological use of the term gender I am referring to, which originated in the United States.

The state of being male or female as expressed by social or cultural distinctions and differences, rather than biological ones; the collective attributes of traits associated with a particular sex; or determined as a result of one’s sex. Also: a (male or female) group characterized in this way.
(Oxford English Dictionary)

Even within this very considerate definition, the binary persists. According to the Oxford English Dictionary it is either male or female, not even including “other” and still very much linked to an individuals biological sex, which (just so you know) can also differ from male or female. As a psychologist, Rose Marie Hoffman moves away from this definition of gender as collective attributes, instead discussing Gender Self-Definition and Gender Self-Acceptance (Hoffman, 2006).

Hoffman describes the stages of the Feminist Identity Model (Downing and Roush, 1985) based on Cross’s Black Identity Development Model (Cross Jr, 1971) as:

(a) unawareness of inequity and discrimination through
(b) experience of crises that force one to confront such inequities to
(c) an immersion and identification with one’s own group that provide opportunities for
reflection and exploration to
(d) integration of one’s experiences around the area of
oppression and a concomitant achievement of balance (i.e., able to evaluate people
as individuals instead of only as group members) and, finally, to
(e) a commitment to meaningful action toward eliminating the ism involved.
(Hoffman, 2006).

This Feminist Identity Development also shows some similarities with the sex role transcendence theory by Rebecca et al., which falls into three phases: a lack of awareness of gender roles, a polarization stage, and the transcendence of gender role stereotypes (Rebecca et al., 1976). The womanist position differs from the feminist position in that it
recognizes “poverty, racism, and ethnocentrism as equal concerns with sexism” (Henley
et al., 1998).

Where does Digital Humanities come in, you might ask yourself. I would like to refer to Nickoal Eichmann, Jeana Jorgensen, and Scott B. Weingarts’ study of fifteen years of DH conferences (2000-2015). They did so

(1) to call out the biases and lack of diversity at ADHO conferences in the earnest hope it will help improve future years’ conferences, and (2) to show that simplistic, reductive quantitative methods can be applied critically, and need not feed into techno-utopic fantasies or an unwavering acceptance of proxies as a direct line to Truth.
(Eichmann et al., 2016)

However, in order to study the lack of diversity, in the name of data quality, they acknowledge “the gross and problematic simplifications involved in this process of gendering authors without their consent or input” (Eichmann et al., 2016). In their own defense they state that “with regards to gender bias, showing whether reviewers are less likely to accept papers from authors who appear to be women can reveal entrenched biases, whether or not the author actually identifies as a women” (Eichmann et al., 2016). I can, to a certain extent, agree that gender bias stems from the perception of a person’s gender, rather than the gender they identify with. The question remains however, whether you can assume others to identify authors as male, female, or unknown/other.

There are several tools to test your own bias, such as https://implicit.harvard.edu/implicit/takeatest.html, and http://www.lookdifferent.org/what-can-i-do/implicit-association-test. Fortunately, and probably after working on my master thesis considering the gender balance in both Computer Science and Digital Humanities for a year, my result suggests “little to no association between female and male and science and liberal arts”. This test again demonstrates a binary definition of gender, confirming my main concern.

Bibliography

Kimberle Crenshaw. Mapping the margins: Intersectionality, identity politics, and violence
against women of color. Stanford Law Review, 43(6):1241–1299, 1991. ISSN
00389765. URL http://www.jstor.org/stable/1229039.

Roopika Risam. Beyond the margins: Intersectionality and the digital humanities. DHQ:
Digital Humanities Quarterly, Volume 9, Number 2, 2015.

Rose M. Hoffman. Gender self-definition and gender self-acceptance in women: Intersections
with feminist, womanist, and ethnic identities. Journal of Counseling and
Development : JCD, 84(3):358–372, Summer 2006. URL http://search.proquest. com/docview/219028098?accountid=17215.

Nancy E. Downing and Kristin L. Roush. From passive acceptance to active commitment:
A model of feminist identity development for women. The Counseling
Psychologist, 13(4):695–709, 1985. doi: 10.1177/0011000085134013. URL http: //tcp.sagepub.com/content/13/4/695.abstract.

William E Cross Jr. The negro-to-black conversion experience. Black world, 20(9):
13–27, 1971.

Meda Rebecca, Robert Hefner, and Barbara Oleshansky. A model of sex-role transcendence.
Journal of Social Issues, 32(3):197–206, 1976.

Nancy M Henley, Karen Meng, Delores O’Brien, William J McCarthy, and Robert J
Sockloskie. Developing a scale to measure the diversity of feminist attitudes. Psychology
of Women Quarterly, 22(3):317–348, 1998.

Nickoal Eichmann-Kalwara, Jeana Jorgensen, and Scott Weingart. Representation at
Digital Humanities Conferences (2000-2015). prepublished, 3 2016. URL 10.6084/ m9.figshare.3120610.v1.