“They’re Not From Here”: Homelessness as a Contestation of Community Identity for Mid-Size Cities
Bibliographic record
Abstract
Homelessness is growing across the global North, and so too is its visibility. Communities are struggling to respond to homelessness and anti-homeless sentiment from some housed people. While much of the scholarly attention focuses on major urban centres, mid-size communities are also grappling with visible homelessness, in some cases for the first time, with less knowledge and fewer resources to manage homelessness than their big city counterparts. In this article, we study a mid-size city in western Canada to examine how perspectives concerning homelessness, crime, and public safety among diverse groups converge and diverge to understand the root cause of the contention around homelessness. Our analysis uncovers how participants’ perspectives about homelessness in their community are intimately tied to claims of a lost small-town identity. These perspectives, we argue, have negative effects on people who are homeless, both personally as victims of violence and criminalization, and socially in the policies and practices to address homelessness. Using Wacquant’s concept of territorial stigmatization, this article reveals how efforts to exclude people who are homeless from the collective community conscience can have the opposite effect, creating the kind of ‘spatial taint’ across the whole city that these revanchist policies and discourses seek to deflect.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.042 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".