How Many Housed People in Calgary are at Risk of Homelessness?
Bibliographic record
Abstract
Significant numbers of people and families in Calgary face financial challenges that put them at risk of homelessness. The authors first define different levels of risk, in order to focus on people at high risk of slipping into homelessness. The authors assume, based on the findings of previous studies, that people faced with financial hardship make all possible budgetary changes to lower their cost of living and thereby retain housing. These changes include using food banks, relying on charities, eating less nutritious diets, living in more crowded conditions, moving to housing with lower rent and giving up any hope of maintaining what the designers of Canada’s poverty line define as a “modest and basic standard of living.” The highest risk category comprises those who have exhausted nearly all efforts to maintain their housing and are extremely vulnerable to even minor shocks to income or living costs. Estimating the number of people and households in Calgary at high risk of homelessnessrelies on key assumptions about housing costs, family structure, food budget, and expenditure reduction. The authors show how their estimates of the number of housed people at high riskof homelessness varies by these assumptions. These calculations provide insight into the effects of rent increases and food inflation on the ability of people with very low income to maintain housing. In doing so, they also provide evidence of how relatively small adjustments to income, rent, and food prices can pull people from the brink of homelessness. The estimates indicate that between 102,635 and 124,375 people in Calgary, including both adults and children, were at high risk of homelessness in 2016. The authors indicate they feel comfortable in supporting a number near the midpoint of this range, approximately 115,000 people, as the number of people at high risk of homelessness in Calgary in 2016. This at-risk population lived in approximately 40,000 households. An estimate for 2023 would need to account for higher rents and food prices but also higher incomes relative to what were observed in 2016. While the authors suggests that the at-risk population is likely higher now than it was in 2016, they note that even were this not true, the 2016 estimate of approximately 115,000 people living in 40,000 households ought to be more than enough to spur policymakers into acting. The encouragement to be found in these calculations is that relatively modest policy interventions have large impacts on the size of the population at risk of homelessness. Consistent with research elsewhere, extreme policy interventions are not required to pull large numbers of people from the brink of homelessness. The most important characteristic of these policy interventions is not their size, but simply the fact they are acted upon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".