Oversight of Police Intelligence: A Complex Web, but Is It Enough?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it