Eviction filings during bans on enforcement throughout the COVID-19 pandemic: an interrupted time series analysis
Bibliographic record
Abstract
OBJECTIVE: Bans on evictions were implemented to reduce the spread of COVID-19 and to protect vulnerable populations during a public health crisis. Our objective was to examine how three bans on eviction enforcement impacted eviction filings from March 2020 through January 2022 in Ontario, Canada. METHODS: Data were derived from eviction application records kept by the Ontario Landlord and Tenant Board. We used segmented regression analysis to model changes in the average weekly filing rates for evictions due to non-payment of rent (L1 filings) and reasons other than non-payment of rent (L2 filings). RESULTS: The average number of weekly L1 and L2 applications dropped by 67.5 (95% CI: 55.2, 79.9) and 31.7 (95% CI: 26.7, 36.6) filings per 100,000 rental dwellings, respectively, following the first ban on eviction enforcement (p < 0.0001). Notably, they did not fall to zero. Level changes during the second and third bans were insubstantial and slope changes for L2 applications varied throughout the study period. The L1 filing rate appeared to increase towards the end of the study period (slope change: 1.3; 95% CI: 0.1, 2.6; p = 0.0387). CONCLUSION: Our findings suggest that while the first ban on eviction enforcement appeared to substantially reduce filing rates, subsequent bans were less effective and none of them eliminated eviction filings altogether. Enacting upstream policies that tackle the root causes of displacement would better equip jurisdictions during future public health emergencies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".