Sex-Disaggregated Systematics in Canadian Time Allocation Committee\n Telescope Proposal Reviews
Bibliographic record
Abstract
Recent studies have shown that the proposal peer review processes employed by\na variety of organizations to allocate astronomical telescope time produce\noutcomes that are systematically biased depending on whether proposal's\nprincipal investigator (PI) is a man or a woman. Using Canada-France-Hawaii\nTelescope (CFHT) and Gemini Observatory proposal statistics from Canada over 10\nrecent proposal cycles, we assess whether or not the mean proposal scores\nassigned by the National Research Council's (NRC's) Canadian Time Allocation\nCommittee (CanTAC) also correlate significantly with PI sex. Classical t-tests,\nbootstrap and jackknife replications show that proposals submitted by women\nwere rated significantly worse than those submitted by men. We subdivide the\ndata in order to investigate sex-disaggregated statistics in relation to PI\ncareer stage (faculty vs. non-faculty), telescope requested, scientific review\npanel, observing semester, and the PhD year of faculty PIs. Consistent with the\nbivariate results, a multivariate regression analysis controlling for other\ncovariates confirmed that PI sex is the only significant predictor of proposal\nrating scores for the sample as a whole, although differences emerge for\nproposals submitted by faculty and non-faculty PIs. While further research is\nneeded to explain our results, it is possible that implicit social cognition is\nat work. NRC and CanTAC have taken steps to mitigate this possibility by\naltering proposal author lists in order to conceal the PI's identity among\nco-investigators. We recommend that the impact of this measure on mitigating\nbias in future observing semesters be quantitatively assessed using statistical\ntechniques such as those employed here.\n
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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 | 0.149 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.028 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".