Homeless Shelter Flows in Calgary and the Potential Impact of COVID-19
Bibliographic record
Abstract
Social distancing and self-isolation are two of the key responses asked of citizens during a pandemic. For people without a home, this advice is rather more difficult to follow. In this article, we use daily data describing the movements of 36,855 unique individuals who used emergency homeless shelters in Calgary over the period 1 January 2014-31 December 2019. We show that the use of emergency shelters is characterized by large flows from and into the broader community and smaller flows between individual shelters. Between admissions of new people into the shelter system and multiple re-admissions of current clients, there were an average of 43,613 movements between the community and between shelters each month. The size of these flows provide a measure of the extent to which people reliant on homeless shelters are exposed to the risk of transmission of coronavirus disease 2019 (COVID-19). By identifying the size and nature of these flows, we hope our analysis helps identify responses that may minimize this population's risk of exposure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".