Gender Distribution of Course Material Authors in a Doctor of Veterinary Medicine Program
Bibliographic record
Abstract
The gender distribution of authors in the health sciences literature has been well documented. We explored whether this distribution persists among library course reserves for a Doctor of Veterinary Medicine program, as course reserves are veterinary faculty members' own teaching materials. Such a bibliometric analysis of course reserves provides a novel method of examining curricular materials. In the fall of 2022, researchers collected the library's current course reserve metadata, including fields such as author names and material types. Binary gender was determined based on a variety of sources: traditional naming conventions, gender presentation in photographs, pronouns in signatures, and biographies. Of the 167 exported authors, 162 were included for further analysis in SPSS. Course reserves' authors were analyzed by collaborators and media type. The dichotomous gender distribution of first authors was 76% male/24% female. Female first authors were more likely to have collaborators than male first authors (39% vs 26%). When collaborations did occur, first and second authors had the same gender at a significantly higher rate. Exploring author gender across material type, we found that generally, the first author gender ratio remained three males to every female. Contextualizing these results in the framework of contemporary health sciences literature, we found that the gender disparities in course reserves to be unsurprising, while still disappointing.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".