Bibliographic record
Abstract
In a recent small sample study, Khazan et al. (2020) examined SET ratings received by one female teaching (TA) assistant who assisted with teaching two sections of the same online course, one section under her true gender and one section under false/opposite gender. Khazan et al. concluded that their study demonstrated gender bias against female TA even though they found no statistical difference in SET ratings between male vs. female TA ( p = .73). To claim gender bias, Khazan et al. ignored their overall findings and focused on distribution of six negative SET ratings and claimed, without reporting any statistical test results, that (a) female students gave more positive ratings to male TA than female TA, (b) female TA received five times as many negative ratings than the male TA, and (c) female students gave most low scores to female TA. We conducted the missing statistical tests and found no evidence supporting Khazan et al.s claims. We also requested Khazan et al.s data to formally examine them for outliers and to re-analyze the data with and without the outliers. Khazan et al. refused. We read off the data from their Figure 1 and filled in several values using the brute force, exhaustive search constrained by the summary statistics reported by Khazan et al.. Our re-analysis revealed six outliers and no evidence of gender bias. In fact, when the six outliers were removed, the female TA was rated higher than male TA but non-significantly so.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".