Studying the gender gap in academic research production among Canadian university professors using the multilevel approach to gender inequalities framework
Bibliographic record
Abstract
The gender gap in academic research production has been demonstrated through various methods and geographical contexts. Previous research has yielded conflicting results concerning the effect of gender as a stand-alone variable on research production and primarily focused on other individual and organizational variables to explain this gap. We seek to clarify the influence of gender-related variables on academic research production, operationalized as journal article equivalents published three years prior to the survey. Using a stratified sample of 2,968 full-time Canadian university professors (49% men, 49% women; 30 to 89 years of age, M = 56, SD = 10.17) from the Academic Profession in the Knowledge Society survey, we analyzed the effect of 15 gender-related predictors on research production and measured the variance of research production explained by the combination of these predictors. Through multiple linear regressions, our findings indicate that gender as an isolated variable is a weak yet significant predictor (ß = .04; p < .05) of research production, and that a large proportion of variance (R2 = .33) is explained by a combination of gender-related variables, such as collaborations, workload division and academic rank. Contrary to what has often been assumed, the gender gap in academic research production is not adequately understood through the effect of gender as a stand-alone variable and gains clarity when we use the ‘multilevel approach to gender inequalities’ conceptual framework, which enlightens the understanding of this phenomenon as a part of the intersection and reinforcement of individual, interactional, organizational, systemic, and cultural institutional factors.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".