'Women in Computing' as Problematic: Gender, Ethics and Identity in University Computer Science Education
Bibliographic record
Abstract
My study is focused on women in graduate Computer Science programs at two universities in Ontario, Canada. My research problem emerges from earlier feminist research addressing the low numbers of women in university Computer Science programs, particularly at the graduate level. After over twenty years of active feminist representation of this problem, mostly through large survey-based studies, there has been little change. I argue that rather than continuing to focus on the rising and falling numbers of women studying Computer Science, it is critical to analyze the specific socio-economic and socio-cultural conditions which produce gendered and racialized exclusion in the field. Informed by Institutional Ethnography – a method of inquiry developed by Dorothy Smith – and by Foucault’s work on governmentality, I examine how specific institutional processes shape the everyday lives of women students. Through on-site observation and interviews with women in graduate Computer Science studies, Computer Science professors and university administrators, I investigate how the participants’ everyday institutional work is coordinated through external textual practices such as evaluation, reporting and accounting. I argue that the university’s institutional practices produce ‘women in computing’ as a ‘problem’ group in ways that re-inscribe women’s outsider status in the field. At the same time, I show that professionalized feminist educational projects may contradict their progressive and inclusive intentions, contributing to the ‘institutional capture’ (Smith) of women as an administrative ‘problem’. Through ethnographic research that follows women students through a range of experiences, I demonstrate how they variously endorse, subvert and exploit the contradictory subject positions produced for them. I illustrate how a North American-based institutional feminist representation of ‘women in computing’ ignores the everyday experiences of ethnoculturally diverse female student participants in graduate Computer Science studies. I argue that rather than accepting the organization of universal characteristics which reproduce conditions of exclusion, North American feminist scholars need to consider the specificity of social relations and forms of knowledge transnationally. Finally, I revisit how women in the study engage with ‘women in computing’ discourse through their lived experiences. I suggest the need for ongoing analysis of the gender effects and changing socio-cultural conditions of new technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".