Gender Differences in Publication among University Professors in Canada*
Bibliographic record
Abstract
Cet article analyse un important sondage à l'échelle canadienne et aborde la probématique de la productivité: pourquoi les professeures d'université publient‐elles moins que leurs collégues hommes ? Les résultats montrent que, dans l'ensemble, les femmes ont publié moins que les hommes — et ce, de manière significative —, à la fois durant leur carrière et au cours des trois années qui ont précédé le sondage. Cependant, des analyses multivariables révèlent que des différences s'avèrent plus prononcées dans les données touchant la carrière que dans celles de la courte période. La plus grande différence entre les hommes et les femmes a trait au fait de publier dans une revue à comité de lecture ou sans, et s'applique à toute leur carrière. Enfin, des différences se laissent expliquer par des différences de rang, d'années depuis l'obtention du doctorat, la discipline, le type d'université ainsi que le temps consacré a la recherche. Des problèmes d'évaluation des prédicteurs de la productivité en recherche sont discutés. This paper analyses a large Canadian national survey of professors and tackles the “productivity puzzle” as to why female scientists publish less than male scientists. Results show that, in aggregate, Canadian female professors have published significantly less than their male counterparts, both over their lifetimes and during the three years before the survey. However, multivariate analyses reveal that gender differences in publication are more pronounced in the lifetime data than in the data for the shorter period. Much of the difference in publication between men and women of the academy is in refereed and non‐refereed articles and reports over their career. Finally, gender differences in publication are largely accounted for by differences in rank, years since PhD, discipline, type of university and time set aside for research. Problems of assessing predictors of research productivity are discussed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".