Bibliographic record
Abstract
Recent research has confirmed that only a minority of people who use emergency shelter beds are long-term users. Most shelter clients stay for short periods and do so relatively infrequently. These people use shelters as a temporary solution to problems that stem from poverty as opposed to problems arising from addiction or mental health problems. The implication is that addressing poverty may be an effective way of shrinking the need for emergency shelter beds. Our study uses information describing demographic characteristics and a measure of housing affordability in 51 Canadian cities to identify to what extent efforts at poverty reduction may enable the closing of emergency shelter beds. Across Canada in 2011, 15,493 permanent beds were available in 408 emergency shelters. The provision of emergency shelter beds varies widely across cities. Calgary, for example, provides more than twice as many beds per 100,000 people than does Vancouver or Toronto and more than four times the number provided in Montreal. The number of emergency beds provided is an indication not only of the number of homeless people but it is also a measure of the local response to the issue. We show that an effective strategy for shrinking the need for shelter beds is to provide improved income support to the very poor. Accounting for differences in climate, housing affordability, and demographics that may be associated with discrimination in housing markets, we show how a relatively modest increase in the incomes of those with very low incomes can shrink the need for emergency beds by nearly 20%. We also show that a modest increase in rent subsidies would have a similar impact. Still other policies that can prove effective are those that reduce the cost of building housing that can be profitably rented at prices those with low incomes can afford. These may involve tax incentives to builders and may call into question efforts at urban densification which makes low-cost construction difficult. The wide range of policy choices means that all levels of government have a role to play in increasing the affordability of housing for those with low incomes. Recognizing the broad range of effective policy options is important because the causes of homelessness vary by city and so policymakers need to be flexible in their responses to the issue. We continue to be perplexed why governments fail to index for inflation the income support provided to those in poverty. That policy alone would go some considerable way toward enabling those with low incomes to stay housed and so reduce the need for emergency shelter beds.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".