Loose Coupling, Burden Shuffling, and Pervasive Penality: The Role of Bylaw Enforcement in Managing Homelessness
Bibliographic record
Abstract
In Canada, over 235,000 people experience homelessness per year. The COVID-19 pandemic has increased the visibility of homelessness and the use of homeless encampments across Ontario. As visibility has increased, so too have community members’ expectations to manage homelessness or find a solution to minimize the visibility of homelessness. While there has been some scholarship about the police management of homelessness, far less is known about the role of bylaw enforcement. Yet, bylaw officers play a critical role in responding to homelessness due to their increasing responsibility for enforcing municipal bylaws and COVID-19 public health mandates, such as stay at home orders and social distancing requirements. This project addresses this gap in the literature by analyzing bylaw officers’ perceptions of their roles and responsibilities when responding to and managing homelessness in their communities. Drawing on 46 surveys and nine in-depth, semi-structured interviews with bylaw officers from across Ontario, I examine how they understand their role in managing homelessness, and how they address complaints about homelessness and homeless encampments. From this analysis, I argue that officers’ organizational mandates and responsibilities, which focus on the regulation of space, are loosely coupled to their roles on the frontline, which require the management and regulation of people. This loose coupling situates bylaw officers in a regulatory ‘grey zone’ where they are left to rely on their experiential knowledge and discretion when responding to homeless complaints. Further, I argue that bylaw officers’ primary goal is to invisibilize homelessness for prioritized community members, and accomplish this mandate by moving people along, often to isolated areas in the municipality. This displacement constitutes a form of pervasive penality, further harming people who are unhoused. I conclude with achievable recommendations for bylaw enforcement agencies and directions for future research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".