Oversight of Police Intelligence: A Complex Web, but Is It Enough?
Bibliographic record
Abstract
This article analyzes the jurisdiction, function, powers, and expertise of oversight mechanisms with reference to capacity to oversee the legality of emerging police intelligence practices such as facial recognition, social media analytics, and predictive policing. It argues that oversight of such practices raises distinct issues ranging from the general oversight of policing, given the secrecy associated with police intelligence generally, to the use of complex software in particular. It combines doctrinal analysis with analysis of interviews with policing intelligence analysts, intelligence managers, lawyers, and IT professionals in three jurisdictions: Canada, Australia, and New Zealand. It brings together the roles of a variety of entities involved directly or indirectly in oversight; in particular, professional standards units, independent police and public sector oversight bodies, intelligence oversight, privacy and human rights regulators, courts, political bodies, contracting parties, and ad hoc bodies. Understanding the web of oversight as a whole, and comparing across jurisdictions, it concludes with specific proposals for reform.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.071 |
| Scholarly communication | 0.031 | 0.040 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".