To tent and protect: Homeless encampments as “protective facilities”
Bibliographic record
Abstract
Post COVID-19, visible homelessness in the form of encampments has grown in cities across North America. Often these encampments are stereotyped as posing health and safety risks. In response to public outcry, many of these encampments have been forcefully removed by city employees and police. However, it is unclear if encampments are criminogenic or simply create that perception. In this study, we use encampment data collected by the City of Brantford (2023) and calls for service and incident data from the Brantford Police Service (2015–2022) to determine if the emergence of encampments results in an increase in crime and disorder in the surrounding areas. We use Thiessen Polygons to approximate encampment area influence. We then analyze changes in crime patterns over time in these areas using a structural break test, from the point of encampment emergence, to determine if encampments significantly increase the likelihood of crime and disorder as compared to previous years in the same areas. Findings suggest that encampments follow the same criminogenic place patterns of other types of facilities. Implications for policy are discussed. • Homeless encampments are assumed to generally correlate with crime and disorder. • Homeless encampments in Brantford generally do not generally relate to significant increases in crime and disorder. • Homeless encampments in Brantford generally relate to no changes or significant declines in crime and disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".