Toronto and the ‘Paris problem’: community policing in ‘immigrant neighbourhoods’
Bibliographic record
Abstract
Since 2005, references to the ‘Paris problem’ have become increasingly frequent among media pundits, urban policy-makers and police agencies to warn about the malaise of Toronto’s low-income, majority non-White neighbourhoods (referred to as ‘immigrant neighbourhoods’). A reference to the rebellion of the French banlieues against state power in France, the ‘Paris problem’ is code for the spectre of ‘race riots’ in Toronto. Here the author looks at the birth of the ‘Paris problem’ and examines the community policing strategies that were rolled out in its aftermath in Toronto. The article demonstrates how these were intertwined with urban policies of social development to which policing was integral. In this, policing needs to be understood holistically as not just coercive in function, but also as ‘productive’; that is, aimed at the manufacture of consent and ultimately of pacification of unruly populations. Underpinning these processes, and also engendered by them, is a racialised and territorialised security ideology crystallised around the figure of ‘the immigrant’ and the conception of ‘immigrant neighbourhoods’. At the heart of such policy-making is a corralling and containing of poor, working-class, ethnically defined communities – youth in particular – that serves to entrench division while maintaining heavy-handed state control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.038 | 0.024 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".