Jurisdictional overlap: The juxtaposition of institutional independence and collaboration in police wrongdoing investigations
Bibliographic record
Abstract
Introducing multiple layers of “independent” structures has become a go‐to strategy for public agent oversight. The question remains whether such decentralized, overlapping structural arrangements of oversight reduce regulatory uncertainty and produce better policy outcomes. Using the case study of Ontario, Canada, I examine the consequences of institutional layering for the specific and broader goal of independent oversight and democratic policing, respectively. Semi‐structured interviews with oversight officials and other key stakeholders as well as past governmental reports that led to the police oversight reform are analyzed to study the gap between the policy intention and outcome. I found that multiple “independent” investigatory agencies are meant to operate concurrently within an integrated system to ensure a responsive and comprehensive oversight system. However, their structural separation obstructs collaboration among the external agencies, causing various dysfunctional bureaucratic behaviors that undermine the overarching intention. The disconnect among different oversight authorities exacerbates their reliance on internal police‐led procedures for all police misconduct inquiries. Implications of my research extend beyond policing and further the study of overlapping regulatory oversight and structural reform through institutional layering.
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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 | 0.026 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".