“Bureaucrats with Badges”: Bylaw Enforcement and the Invisibilization of Homelessness
Bibliographic record
Abstract
ABSTRACT Homelessness affects at least 25,000 people every day in Canada alone. Although research has documented responses to homelessness involving the police and private security, there is much less scholarship investigating municipal bylaw enforcement officers’ role in the governance of homelessness. We explore how bylaw officers regulate homelessness in Ontario, Canada. Drawing on surveys and semi-structured interviews with bylaw officers, our analysis demonstrates that bylaw officers have been called upon to manage a “crisis of complaints” related to the increasing visibility of homelessness across Ontario. To manage these complaints, bylaw officers rely on burden shuffling, first, moving people along because it is the most efficient way to manage homelessness complaints in their jurisdiction. Bylaw officers also engage in bureaucratic burden shuffling, reclassifying complaints to other agencies. We argue that, through their mechanisms of enforcing public space orders, bylaw officers engage in reluctant criminalization using invisibilization tactics. These strategies constitute another form of pervasive penality, or a punitive process of policing, through move along orders and threats of arrest, ultimately leading to the invisibilization of homelessness. Such responses increase the precarity that often characterizes unhoused people’s lives and misrepresents homelessness as a deviancy issue rather than a human rights violation.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".