Mind the Gap. Women Authors in Anglophone Classical Scholarship, 1970–2016
Bibliographic record
Abstract
Although women have a long history of contributing to classical scholarship, they continue to be a minority both among faculty members and scholarly authors. In this paper, I compare the proportion of women employed at Classics departments in the US, Canada, UK, and Ireland with their proportion among the authors of a sample of English journal articles. While the overall institutional gender balance is approaching parity, women continue to be underrepresented in senior positions, and progress seems to have stalled over the last ten years. In addition, my analysis of the L’Année philologique database demonstrates that while the share of articles written by women has greatly increased from 1970 to 2009, it has remained stagnant since, hovering just around the 28% mark. I hypothesise that the main reason for women’s continued underrepresentation in Classical scholarship, apart from unconscious biases, is the disproportionate share of care responsibilities shouldered by women both within and without academia. In order to improve the situation, I propose a series of interventions to be taken by journal editors and university administrators, particularly the introduction of quotas.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".