Patterns and Intensity of Use of Homeless Shelters in Toronto
Bibliographic record
Abstract
A large administrative data set allows us to examine shelter use by single adults, youth, and families in Toronto. We find important differences in shelter use by single adults, youth, and families. We introduce an approach that allows us to identify a noticeable increase in the percentage of shelter clients whom we define as chronic users of the shelter system—people for whom each episode of shelter use is typically very long. This should be a concern because chronic users of the system, although they make up only a small fraction of all shelter clients, fill more than 40 percent of shelter capacity. A growing number of chronic shelter users will strain the ability of the shelter system to provide shelter to those seeking temporary relief while they re-establish themselves into housing. Homeless shelters are a response to a serious social problem. They are not the cause of nor are they the solution to that problem. Growing shelter use is an indication of a social order in trouble.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".