Who Are the Homeless? Numbers, Trends and Characteristics of Those Without Homes in Calgary
Bibliographic record
Abstract
In 2008, Calgary was the first city in Canada to institute a 10-year plan to end homelessness. The plan was introduced in part due to the steady and rapid growth in homelessness in the city since 1992. Since 2008 growth in the number of homeless people has stopped despite a rapidly growing city. The number of people enumerated as homeless by point-in-time counts has fallen from 304 persons per 100,000 population to 256 persons per 100,000 population in 2014, a drop of more than 15 per cent. Looking beyond simple counts of the number of homeless people, we examine how people who are homeless use emergency shelters. Tracking shelter use over a five year period by nearly 33,000 individuals, we find that, contrary to what might be thought to be true, the great majority (86%) of people who use emergency shelters in Calgary do so very infrequently and for only short periods of time. Visiting shelters less than twice (on average), these “transitional” users stayed in shelters for an average of only 15 days spread during the five years of our study. Another 12% of people used emergency shelters more frequently; an average of 8 times spread over five years. These “episodic” users stayed for a total of 113 days on average. Only a tiny minority, just 1.6% of all shelter users, stayed in shelters for very long periods. These “chronic” users visited shelters an average of three and a half times and stayed a total of 928 days over the five years of our study. Because they stay in shelters for long periods, chronic shelter users occupy one-third of shelter beds. The implication of this is that finding stable, supportive housing for just 1.6% of those experiencing homeless – a total of about 900 individuals in Calgary -- would free-up one-third of beds in emergency shelters. Providing supportive housing for episodic users as well would free-up another one-third of beds and so enable shelter providers to focus on their main function as providers of emergency housing. Moving people from emergency shelters into supportive housing delivers savings in the form of reduced interactions for these people with the criminal justice and healthcare systems; savings that have been shown in other studies to significantly off-set the cost of supportive housing. Planning to end homelessness has always been an ambitious goal. While the homeless serving community has made significant gains in understanding how best to solve the problem, greater effort may be required of local, provincial and federal policy makers to find ways of resolving the issue that is at the heart of Calgary’s homelessness problem; namely, the lack of affordable rental accommodations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".