Misconduct Mismanagement: Independent Oversight, Accountability, and the Rule of Law
Bibliographic record
Abstract
Over the last few decades, there has been increasing interest in research and theory on police oversight. Likewise, different types and layers of oversight mechanisms have been introduced to advance the democratic policing ideal. Unfortunately, the endless cases of police wrongdoing, oversight decisions that are almost always stacked against members of the public, and a series of civil protests across the Western world against police brutality and abuse of power suggest that the added layers of external oversight have yet to deliver the promised outcome. Some elements of democratic policing—such as the use of minimal coercion, respect for human rights, justice, equality, and responsive policing—explicitly relate to how policing services are delivered through the behaviours and decisions of individual police officers. Others—such as independent oversight, accountability, and the rule of law—are directly relevant to the control mechanisms that moderate their discretionary authority. Most existing studies on police oversight, such as those focused on social psychological theory of procedural justice, lack conceptual clarity on these elements. The majority of studies on this topic overlooks the role of institutional design and dynamics in relation to the overarching aim of democratic policing as they primarily focus on individual-level subjective evaluations and the willingness to obey. My dissertation conducts a case study of Ontario, Canada. It takes a qualitative approach using primarily case law, governmental reports, and semi-structured interviews with the oversight officials and other key stakeholders as data sources. I analyze the practices and implications of having heterogeneous external oversight mechanisms within the same regulatory space for democratic policing. I outline several inconsistencies within the democratic policing framework and shortcomings of the police oversight system that relies on a heterogeneous network of arm’s length authorities. I find that both the rules and values enshrined in the oversight system—however all-encompassing and -promising they may sound—permit considerable room for interpretation; and the same logic and formula that failed the previous oversight regime continue to paralyze the new oversight arrangements.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".