Research Note: Revisiting the Collection of “Justice Statistics by Race” in Canada
Bibliographic record
Abstract
The debate over the collection of justice statistics by race continues to hinge on the same key issues that were central to the debate when it arose in the early 1990s. There has been one major change, however: whereas racial minority groups were once vehemently opposed to the collection of justice statistics by race, for fear that such statistics would be used to justify discriminatory policies, many minority groups are now advocating for the collection and publication of this data as a means to redress racial discrimination in the administration of justice. Having discussed the lack of available data on racial and ethnic statistics in the Canadian justice system, the authors sought support from the Canadian Law and Society Association (CLSA). At the 2009 annual general meeting of the CLSA, a motion for the association to take an official position in support of the collection of justice statistics by race was put forth by the authors and accepted by the association. At this time it was also decided that a committee would be established to conduct relevant research and to lobby for the collection of pertinent data. At present we are asking interested individuals or organizations who fall into one or more of the following categories to contact the first author: (1) Those with arguments relating to the collection of justice statistics by race that have not been articulated in the debate that has taken place over the past two decades. (2) Those with information pertaining to the collection of justice statistics by race that is not readily available or that has not been documented in the academic work referenced herein. (3) Those who are interested in participating in the work of the committee outlined at the end of this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.093 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.041 | 0.014 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.006 | 0.014 |
| 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".