The Law Practice Program: Tackling Racial Inequality in the Legal Profession?
Bibliographic record
Abstract
In 2014, the Law Practice Program (LPP) was introduced in Ontario, creating an alternative to the traditional articling process. The authors consider the demographic make-up of the first cohort of the French LPP and its access to justice implications. Their survey showed that French LPP candidates were overwhelmingly racialized. Moreover, a high percentage of the candidates were 1) born outside of Canada, 2) older than the average law student and 3) male. While the statistical pool is small and although these are very early days for the LPP, the survey results suggest that the traditional articling avenue may not be fully accessible to candidates with certain personal characteristics, and that the LPP may play an important role in addressing some of those barriers. At the same time, however, the authors are concerned that unless special care is taken, the LPP could reinforce some of the existing challenges that racialized lawyers face within 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".