Public relations, repression, and counter-insurgency: the overlapping functions of police ‘co-response’ teams in Montréal
Bibliographic record
Abstract
In the last twenty years, police ‘co-response’ teams have become an increasingly common feature of urban policing. As the term suggests, co-response teams bring together police and non-police personnel to train, patrol, or otherwise operate together. The involvement of non-police personnel (e.g. social workers, mental health professionals) can seem to contribute to a different approach than conventional policing: more supportive, less repressive, and more problem-oriented. The literature on co-response teams, however, remains sparse and disconnected from relevant critical literatures. To address this limitation, this article situates co-response teams within the larger literature on community policing. Connecting insights from this larger literature to an empirical analysis of co-response teams in Montreal, Canada, the article argues the teams carry forward the core functions of conventional policing and, in fact, seek to resolve an ongoing crisis in police hegemony in three ways: (1) they are a public relation scheme that allows unreformed police practices to persist undisturbed; (2) they are a strategy to gather consent for expanded police repression; and (3) they are a form of domestic counter-insurgency, which serves to neutralize sites of political resistance against police power.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".