“When We Do Evict Them, It’s a Last Resort”: Eviction Prevention in Social and Affordable Housing
Bibliographic record
Abstract
Evictions are a common contributing factor to homelessness and are experienced overwhelmingly by vulnerable populations, including low-income households, single parents, and minority groups. At the same time, social and affordable housing providers serve increasingly vulnerable populations. Although all evictions are potentially problematic, those that occur in social and affordable housing can carry particularly severe consequences. Little research exists on evictions in social and affordable housing, and there is even less on eviction prevention practices in this sector. This project seeks to fill this research gap by exploring emerging eviction prevention practices in social and affordable housing in Edmonton, Alberta, Canada. Our findings show that evictions are a complicated process for both tenants and housing providers, and most commonly occur because of rent arrears. Housing providers try to prevent evictions, and toward this end, they have adopted four broad eviction prevention practices, centered on financial management, regular communication with tenants, provision of tenant supports, and community development. However, housing providers are often constrained in their ability to prevent evictions, in particular by human resource and financial limitations. These challenges lead to complex negotiations between housing providers’ social mandates to provide affordable housing to vulnerable households and their regulatory and operational environments.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| 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".