CETA - where are the women? Diffusing the thought-terminating clichs that impeded diversity
Bibliographic record
Abstract
Given the enormity of the arbitrator's role, one might expect that appointers would create a robust new roster for each unique case. Focus would be placed on skills and talent, rather than proxies like race (white), gender (male), and age (old) to predict whether a neutral will do well. This Chapter discusses merit-based roster-making by reference to Dr. Katherine Simpson's 2020 submissions to the EU and Canada in response to the under-representation of women in the List of Arbitrators under Article 29 of the CETA. Dr. Simpson used a three-step approach based on "discretion elimination" to mitigate unconscious or implicit bias and, ultimately, to produce an alternative roster of 70 women whose credentials made them comparable, if not interchangeable, with those appointed to the CETA roster. Using this 3-step approach, researchers will give themselves the best chance of finding excellent neutrals for their disputes, without unintentionally excluding "diverse" candidates.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".