Still Working in the Shadow of Men? An Analysis of Sex Distribution in Publications and Prizes in Canadian History
Bibliographic record
Abstract
This project was inspired by the Canadian Historical Association’s June 2014 awards ceremony at which the majority of prize winners were men. Why, we wondered, are so few women awarded prizes outside the areas of women’s history, the history of sexuality, and the history of childhood and youth? First, we asked who is working in history? To what degree have departments achieved gender parity in hiring? Do women produce work at a rate proportional to their presence in departments? We collected data in three categories: book reviews, other journal content, and books published between 2004 and 2013. We found that women produce fewer books than do men. Women’s books are less likely to be reviewed than are books written by men and few men review books written by women, a fact with significant implications for both advancement and the inclusion of women in the wider curriculum. Women produce a number of articles proportionate to their presence in the discipline; we suggest that this is because articles require less time of one’s own than do books. It is time to revisit openly and explicitly how academic excellence is determined, and how structural forces produce the sexual inequalities documented here and elsewhere.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.040 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".