Socio-spatial insights into evictions governance and tenant movements during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic and related economic effects have served to thrust rental housing insecurity into the public spotlight. Documenting the extent of pandemic displacement in the City of Ottawa and province of Ontario , Canada, this article provides insight on evictions governance, urban marginality, and social struggle. The socio-legal developments surrounding pandemic evictions offer a compelling case in which to analyze the governance of urban marginality in its various intricacies. During the pandemic, the Ontario government passed legislation to protect tenants from eviction, yet also passed legislation that criminalized tenants organizing against evictions. Tenants engaged in informal actions to stop evictions were met with the threat of formal legal sanctions; evictions moratoria—as a mechanism of care—were coupled with punitive forms of urban marginality governance, such as through evictions tribunals and the criminalization of dissent. Using a mixed-methods approach, we temporally and spatially map the scale and measure the impact of pre- and post-pandemic evictions—documenting that evictions tend to occur in areas with high core housing need and racialized neighbourhoods. We also examine the emergence of new social movements to fight displacement and assess the varied government and landlord responses—including evictions moratoria, tribunal eviction blocks, and the criminalization of tenant organizing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".