Profiling the Future: The Long Struggle against Police Racial Profiling in Montreal
Bibliographic record
Abstract
Racial profiling is an increasingly well-studied problem in Quebec. Efforts to combat this problem, in contrast, are virtually non-existent. Seeking to address this gap, this article examines the long struggle to combat racial profiling in Montreal. This struggle began in earnest in 1979. It saw its most significant achievements between 1984 and 1991 and it saw most of these achievements watered down and rolled back between 1992 and 1997. The struggle was finally reborn in 2008, with an activist-led movement that led to a new strategic plan on racial profiling in 2018. Tracing the history of this struggle serves two purposes. First, it reveals the divergence between the narrow range of measures adopted by the City of Montreal and the wider range demanded by Black, anti-racist, and other progressive actors. Second, it shows that the current strategic plan, while touted as “the most ambitious and far-reaching” effort in the city’s history, is actually a diluted version of the actions taken between 1984 and 1991, actions that did little, if anything, to reduce racial profiling. The present strategic plan signals a failure to learn from the past and points toward a pair of possible futures: one in which the status quo endures and racial profiling continues unabated, and one in which social forces are transformed, driven by the actions of people and organizations that have demanded more from the City, and that proposes a new political vision and a wider set of actions.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".