Profiling a problem in Canadian police leadership: the Kingston Police data collection project
Bibliographic record
Abstract
Abstract: Following a series of events that highlighted the need for action on the part of the Kingston Police to address perceptions of racially‐biased policing, a data collection project was inaugurated with the assistance of a criminologist from the Centre of Criminology, University of Toronto, to specifically quantify the racial and/or ethnic origin of all individuals stopped by Kingston Police officers in “non‐casual” situations. This article traces the “precipitating events” that led to this project, the definitions relevant to racially‐biased policing, racial profiling, and bias‐free policing, as well as providing some background on current research and practice in these areas in Great Britain and the United States. There is a consideration of the context within the Kingston Police data collection project operated, including previous commissions of inquiry in Ontario that made recommendations in support of such practices, and recent testimony before the Senate Special Committee on the Antiterrorism Act which involved an examination of matters pertaining to racial profiling. The article challenges Canadian police leaders to consider the value of replicating the Kingston Police data collection and offers some insights derived from being the first police service in Canada to undertake such an initiative. Sommaire: À la suite d'une série d‘événements qui ont souligné le besoin d'agir de la part de la police de Kingston pour répondre aux perceptions de préjugés fondés sur une politique d'inégalité raciale, un projet de collecte de données a été inauguré avec l'assistance d'un criminologiste du Centre de criminologie de l'Université de Toronto pour évaluer avec précision l'origine raciale ou ethnique de toutes les personnes arrêtées par les agents de la police de Kingston dans des situations “non occasionnelles”. Cet article retrace les événements qui ont “précipité ce projet, les définitions pertinentes aux préjugés racistes dans les forces de police, le profilage racial, les services de police impartiaux, et il fournit aussi des informations de base sur la recherche et la pratique actuelles dans ces domaines en Grande‐Bretagne et aux États‐Unis. L'article étudie également le contexte dans lequel le projet de collecte de données de la police de Kingston a fonctionné, y compris les commissions d'enquête menées précédemment en Ontario présentant des recommandations en faveur de telles pratiques, et les témoignages déposés récemment devant le Comité spécial du Sénat sur la Loi contre le terrorisme qui ont comporté un examen des questions pertinentes au profilage racial. L'article met les leaders de la police canadienne au défi d'examiner si cela vaut la peine de reproduire la collecte de données de la police de Kingston et présente aussi des réflexions sur cette initiative qui fut une première dans les services de police au Canada.
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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 | 0.042 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.023 |
| Science and technology studies | 0.031 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".