Flexible Temporalities, Flexible Trajectories: Montreal’s Nursing Home Crisis as an Example of Temporary Workers’ Complicated Urban Labor Geographies
Bibliographic record
Abstract
This chapter analyzes how home and work intersect at long-term care facilities in Montreal, spaces that function as both places of residence and employment. It highlights the relationship between poor housing conditions and precarious employment. It cites that workers at long-term care facilities in and around Montreal have few housing options and tend to live far from the facilities they work in. It also notes the ways in which the virus moves between facilities scattered throughout the region and the low-income neighborhoods where many care workers reside. The chapter articulates that residents of the care homes constituted 70 percent of Quebec's COVID-19-related deaths during the first wave of the COVID-19 pandemic. It functions as an example of how the COVID-19 crisis can be used to reimagine data collection on employment geographies, in particular for low-wage jobs with spatial patterns that are difficult to predict.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".