Commentary on ‘Four types of gender bias affecting women surgeons, and their cumulative impact’ by Hutchison
Bibliographic record
Abstract
The central concerns of Hutchison’s1 paper are the under-representation and unequal pay of women in surgery and the role that subtle gender biases play in explaining these phenomena. My comments will focus on how well executed and important this work is and also why we need more of it to fully understand the gravity of the situation for women in surgery and how it compares with similar situations for women in other fields. Hutchison argues that women in surgery experience many subtle inequities that together help to explain their unequal representation and pay relative to men. She conducted a qualitative study that involved in-depth interviews of 46 women surgeons: fellows or trainees of the Royal Australasian College of Surgeons (RACS). Effort was taken to recruit participants who were diverse in various ways, including in their perspectives on sexual harassment in surgery. They were asked about barriers they had encountered in their careers but not specifically about whether they were targets of gender bias, implicit bias or the like. Despite not being prompted to do so, they gave responses that revealed consistent patterns of explicit sexism and of implicit gender bias, including bias that results in epistemic injustice (ie, injustice in how one is treated as a knower). For example, the …
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.011 |
| 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".