Celebrating 30 Years of the Indigenous Blacks & Mi’kmaq Initiative: How the Creation of a Critical Mass of Black and Aboriginal Lawyers is Making a Difference in Nova Scotia
Bibliographic record
Abstract
Drawing on my own experience as alumni of the Indigenous Blacks & Mi’kmaq Initiative at the Schulich School of Law at Dalhousie University—one of the only dedicated access program in a Canadian law school for Black and Aboriginal students—I argue that such programs create optimal conditions for fostering greater awareness of critical race issues within the legal profession. The reason for this is that such programs create a critical mass of Black and Aboriginal law students and alumni, who support and encourage each other and, as a result, acquire confidence and skill in raising, and educating others about, critical race issues within the various professional positions they hold. I believe that such programs are fundamental not only to increase representation within law schools, the legal profession and the judiciary but to creating lawyers leading positive change for their communities and society more generally.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.048 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".