Implications of Board Motions Relating to Contact Cards and Quarterly
Bibliographic record
Abstract
The Canadian Civil Liberties Association (CCLA) is a national organization with thousands of supporters drawn from all walks of life. The CCLA was constituted to promote respect for and observance of fundamental human rights and civil liberties and to defend and foster the recognition of those rights and liberties. The CCLA’s major objectives include the promotion and legal protection of individual freedom and dignity against unreasonable invasion by public authority, and the protection of procedural fairness. For almost 50 years, the CCLA has worked to advance these goals, regularly appearing before legislative bodies and all levels of court. It is in this capacity, as a defender of constitutional rights and an advocate for the rights and liberties of all individuals, that we make submissions to the Toronto Police Services Board (TPSB) with respect to the difficult problem of race-based harassment (commonly known as “racial profiling). CCLA continues to be concerned about race-based harassment. Race-based harassment is generally defined as including any or a combination of any of the following actions by police against black male youth and/or others from racialized and/or marginalized communities: unwarranted – questioning, stops, identity collection, intimidation, searching, and aggression. Race-based harassment is unacceptable in a society committed to equality before the law. While
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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 | 0.012 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".