Implicit bias, women surgeons and institutional solutions: commentary
Bibliographic record
Abstract
This paper argues that a major contribution to women’s under-representation and the gender pay gap in surgery is the interaction and aggregation of many small wrongs, or as they have come to be called in the literature, microinequities. Further, the paper argues that existing strategies do not adequately address the problems faced by women surgeons and cannot do so without an understanding of those wrongs as microinequities. Insights from the literature on ethics and microinequities are thought to be able to inform new strategies.1 The study identifies four different kinds of gender bias: workplace discrimination, epistemic injustice, stereotyped roles and objectification. The different kinds of gender bias interact with one another and add up in ways that pose serious setbacks to the careers of women surgeons. In addition to being small wrongs, microinequities share other features. They are cumulative; they interact with one other; they are often invisible; and they are implicit or unintended. My comments are going to focus on the response to microinequities, …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.014 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".