Balancing the Byline: Exploring Gender and Authorship Patterns in Canadian Science Publishing Journals
Bibliographic record
Abstract
Canada is internationally recognized for its leadership in science and its commitment to equity, diversity, and inclusion in STEM (science, technology, engineering, and math) fields. Despite this leadership, limited research has examined gender disparities in scientific publishing within the Canadian context. This study analyzed over 67 000 articles submitted to 24 Canadian Science Publishing (CSP) journals between 2010 and 2021 to better understand patterns of gender representation. Findings showed that women accounted for less than one-third of published authors across CSP journals. Representation varied by discipline, with higher percentages of women in biomedical sciences and lower percentages of women in engineering, trends that mirror national and global patterns. Notably, the percentage of women submitting manuscripts closely matched those published, suggesting that broader workforce disparities may play a larger role than publication bias. Women were less likely to be solo authors or hold prominent authorship positions, such as first or last author, roles typically associated with research leadership and career advancement. These findings point to the need for a two-fold response: continued efforts to address systemic barriers to women's participation in STEM, and a review of publishing practices to identify effective strategies that ensure equitable access, recognition, and inclusion for all researchers.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.028 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".