Homelessness in Alberta: The Demand for Spaces in Alberta’s Homeless Shelters
Bibliographic record
Abstract
Homelessness in Alberta is overwhelmingly concentrated in Calgary and Edmonton, with almost two-thirds of total provincial shelter usage in the former. Calgary also experiences much greater fluctuations in shelter use. Three interconnected economic factors — the supply of rental accommodations, the state of the labour market and the inward flow of jobseekers — go a long way toward explaining both Calgary’s unusually large share of Alberta’s homeless as well as the swings in shelter use. Calgary has proportionately less than half as many rental units as Edmonton and this gap is widening. Simultaneously, Calgary, more than any other Canadian city, attracts a significant share of migrants during times of economic growth increasing demand for affordable housing and then shelter space when the availability of housing approaches zero. The recent fall in shelter use in Calgary (and so Alberta) may therefore prove temporary should a recovering economy attract more arrivals and so drive up shelter use again. The provincial government’s recent efforts to increase the stock of affordable housing are appropriate but greater progress could be made if it devised ways to enlist the energy and efficiency of the private sector to expand Calgary’s rental market.
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 | 0.000 | 0.001 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".