Homeless Shelter Flows in Calgary and\nthe Potential Impact of COVID-19
Bibliographic record
Abstract
La distanciation physique et le confinement sont deux des principaux comportements que l'on demande aux citoyens d'adopter en temps de pandemie. Pour les sans-abri, ce conseil est relativement plus difficile a suivre. Les auteurs utilisent des donnees quotidiennes decrivant les mouvements de 36 855 personnes differentes ayant eu recours aux refuges d'urgence pour sans-abri a Calgary entre le 1er janvier 2014 et le 31 decembre 2019. Ils montrent que le recours aux refuges d'urgence se caracterise par d'importants mouvements d'usagers en provenance et a destination de la collectivite en general et des mouvements plus modestes d'usagers entre les differents refuges. Pour etablir un parallele entre les admissions de nou-veaux clients dans le systeme des refuges et les multiples readmissions de clients existants, ils notent que la moyenne mensuelle des mouvements entre la collectivite et les refuges et entre les refuges eux-memes s'etablit a 43 613. L'envergure de ces mouvements fournit un indicateur de la mesure dans laquelle les per-sonnes qui comptent sur les refuges pour sans-abri sont exposees au risque de transmission de la maladie du coronavirus 2019 (COVID-19). En definissant l'ampleur et la nature de ces mouvements, les auteurs souhaitent faciliter, grace a leur analyse, l'elaboration de solutions pour minimiser le risque d'exposition de cette population. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".