Policy Trade-Offs in Decisions on the Provision of Emergency Homeless Shelter Beds in Canada
Bibliographic record
Abstract
Why do some cities provide many emergency shelter beds while others do not? We interpret differences with respect to the supply of shelter beds as originating from alternative choices concerning a series of policy trade-offs. These trade-offs arise because of climate effects, prejudice, levels of poverty, and housing and labour market conditions affecting the rates of homelessness. Governments have policy levers they can adjust to respond to changes in most, if not all these conditions. One of these policy levers is the number of emergency shelter beds that are made available. But other policy responses are also possible. These include efforts to reduce poverty, policies designed to increase the stock of affordable housing, and policies to increase income support payments. Choosing to supply shelter beds over other policy responses reveals a preference for one set of societal outcomes over another. Using data describing conditions in 52 Canadian cities, we test six hypotheses describing why some communities choose to provide more homeless shelter beds than others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".