Bibliographic record
Abstract
In the October (2009) issue of Lexpert you feature the recipients of the Zenith Awards – “Celebrating Leading Women Lawyers”. It is no play on words, but the cover of your magazine is markedly ‘white’.While the recipients are undoubtedly deserving individuals, there is a clear absence of visible minorities at the end of the “labyrinth” (as you have put it). In keeping with the metaphor, it seems as though female visible minorities were never given a map, or a compass, from their white female (or male) colleagues.I think that it a disservice to the hundreds of visible minority women lawyers in Canada to not include even one (1) of them in your list.This is not about tokenism. It is about recognizing the signal that is sent to young, visible minority female lawyers (and law students). It sends the message that they are not among the ‘zenith’. It further emphasizes the dual discrimination that female visible minorities experience (as being both female, and a minority). Discrimination comes in many forms. And I think the selection process for the current recipients of the Zenith Awards demonstrates how ingrained and systemic it truly is in the legal profession.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.040 | 0.036 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".