Taking the Law to the Streets: Legal and Spatial Tactics Deployed in Public Spaces to Control Protesters and the Homeless in Montreal
Bibliographic record
Abstract
Homeless people and protesters in Montreal have at least one thing in common: both groups (and they are by no means mutually exclusive) are routinely controlled for their occupation of public spaces through tickets issued by the municipal police for alleged violations of municipal by-laws (such as loitering, drinking alcohol in public, or unlawful assembly).My doctoral research project brings a municipal and ethnographic dimension to the analysis of local governance of public spaces in Montreal. I argue that two specific groups of marginalized people –namely homeless people and protesters– are disproportionately ticketed by the municipal police because they are seen as disorderly people making atypical uses of public spaces. The ticketing practices, anchored in broken windows theory and order maintenance policing, serve to remove homeless people and protesters from public spaces. Under the appearance of inoffensive space management, serious exclusion occurs and multiple rights are violated. In the end, I show that banal and seemingly unthreatening legal processes, such as those involved in the issuing of tickets in Montreal, can have a profound impact on people’s lives but also on the way we construct public space and experience life in the city.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".