The Impact of Gender on Researchers’ Assessment: A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Women remain underrepresented in Dentistry in academia, and this gap is widened whenever each career step is progressed. It is of utmost importance to investigate underlying associated factors to predict researchers’ assessment of their gender in Dentistry and the overall STEM (Science, technology, engineering, and mathematics) fields. Thus, we developed a randomized controlled trial to test whether women or men would be preferred with identical curriculum vitae (CV); and the impact of the career stage in the evaluators’ choice. To this, a simulated post-doctoral process was carried forward to be assessed for judgment. Level 1 and 2 Brazilian fellow researchers in the field of Dentistry were invited to act as external reviewers in a post-doctoral process and were randomly assigned to receive a female or male CV. They were required to rate the CV from 0 to 10 in scientific contribution, leadership potential, ability to work in groups, and international experience. For all categories of CVs evaluated, men received higher scores compared to the CVs from women. Robust variance Poisson regressions demonstrated that men were more likely to receive higher scores in all categories, despite applicants’ career stage. For example, CVs from men had nearly three quarters more likely to be seen as having leadership potential than equivalent CVs from women. Gender bias is powerfully prevalent in academia in the dentistry field, despite researchers' career stage. Actions like implicit bias training must be urgently implemented to avoid (or at least decrease) that more women are harmed.
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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 | 0.041 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".