Toronto Homeless Shelters Occupancy Levels & Transit Accessibility
Bibliographic record
Abstract
Homelessness in the City of Toronto is an ongoing problem. While the number of homeless seems to have remained steady, the number living on the streets has actually increased (Peat & Chief, 2013). In fact, this past winter of 2014-2015 has seen the deaths of three homeless men as a result of sleeping on the streets during extreme cold weather (Colbert, 2015). As a result of these facts it is necessary to look at options homeless individuals have for obtaining shelter as well as the distance they have to travel to get to a shelter. Our research project has focused on occupancy levels in homeless shelters during “extreme cold weather alert” days in the City of Toronto, as well as access to the homeless shelters using public transit and by walking from various points in Toronto where homeless people spend the day. The goal of this research is to see how accessible homeless shelters are to public transit routes as most homeless individuals will be using public transit or walking to get to the shelters for the night. Also, to look at how accessible the shelters are to spaces homeless individuals stay during the day, such as public libraries and warming centers. Finally, with the recent deaths due to homeless individuals sleeping on the streets during extreme cold weather events, it is necessary to see what the occupancy levels can be at the shelters and if the issue is a shortage of beds or another unrelated issue.
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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 | 0.000 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".