The ‘regulatory grey zone’: bylaw enforcement’s governing of homelessness and space
Bibliographic record
Abstract
Over the past two decades, homelessness has become more visible, and with it are increased demands for law enforcement to minimise the visibility of people experiencing homelessness, and manage, or ultimately remove, local encampments. While scholarship exists on police responses to homelessness, the role that other security actors, such as municipal bylaw officers, play in managing and regulating homelessness is largely unknown. In this paper, we explore municipal bylaw officers’ perceptions of their roles and responsibilities related to homelessness in Ontario, Canada. Our analysis reveals how bylaw officers have become important players in the security governance of homelessness. We demonstrate how bylaw officers’ policies, which focus on the regulation of space, are loosely coupled with, or disconnected from, their frontline activities, which require the regulation of people. This loose coupling situates bylaw officers in a perceived regulatory grey zone, requiring them to use discretionary solutions informed by their subjective experiences to govern people experiencing homelessness. The reliance on subjectivity and discretion expands security networks regulating and governing people experiencing homelessness.
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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 | 0.009 |
| 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 | 0.048 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".