Bibliographic record
Abstract
Women's proportional representation in university Computer Science (CS) programs in Canada has been declining. This study set out to (1) provide a clear picture of the recent research into women and computing education and work; (2) develop a model based on past research and the theoretical perspectives of dual-systems theory, social closure, and social control; (3) assess the impact of social controls on women's education and work choices through interviews with computer workers; and (4) compare the trends in computer education and work over time. This study finds support for theories of social closure and control: interviews show that, over time, factors vary in their influences on women's computing career choices. The increasing status of computing work, the broadening applications of computing, the growing shortage of workers, and the narrow entry into CS affect beliefs about computing. Respondents' belief in myths that computing careers involve very little human interaction, and that women lack the kind of curiosity required of a computer scientist, stopped many from entering CS. The encouragement of mathematics teachers, and gaining computer-related experiences positively influence women to study computing. The alternative route to computing through a non-technical job shows that gaining computer experience even in the workplace can influence women making career choices. Women's proportions are increasing in non-university computer education as women's proportions in CS have been declining. Women taking alternative routes to computing careers tend to work in the less technical occupations, and are segregated by sex within high technology industries. They are more likely to work part-time, are clustered in lower paying specialties, and earn less income than men. The income gap is small and narrowing for women with high computer skills. Despite the benefits of high skill computer work, women are increasingly preparing themselves for medium and lower skill computing work through non-university education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 source (direct Gemma or distilled Codex), 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".