The governance gap: examining the capacity of police service boards to hold police services accountable in Canada
Bibliographic record
Abstract
In the wake of the Defund the Police and the Black Lives Matter (BLM) movements, police accountability and legitimacy are commanding a significant amount of attention. Importantly, questions are being raised about how to effectively govern and manage policing especially with respect to police violence and misconduct. While much of this discussion has focused on the actions of the police, there has been little research examining the civilian bodies responsible for holding police accountable: Police Service Boards (PSBs). In recent years, a few high-profile public reports have identified that certain PSBs in Canada, are struggling to carry out their roles and responsibilities and offered numerous recommendations to address existing shortcomings. These detailed reports have important implications for oversight and governance. However, the scale and generalizability of the response to the concerns raised in these reports remains unknown. In this study, we explore issues of capacity and training for PSB members to better understand the gap between the expectations of PSBs to provide meaningful governance of the police and their perceived capacity to do so. Our research suggests that a significant gap in governance exists, related to the lack of adequate training and capacity building in PSBs across the country. Recommendations and future directions are discussed.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".