Bibliographic record
Abstract
Most people with income below Canada’s official poverty line avoid homelessness. They do so by making extraordinary efforts to minimize out-of-pocket expenses and so conserve income for rent. These efforts involve moving to less expensive accommodations, living in more crowded housing, forgoing non-necessities, and relying on charities to reduce expenditures on food, clothing, and other necessities. These efforts to minimize expenditures mean that the poverty line is a poor measure of the income required to avoid homelessness. The absence of such a measure means we are unable to determine whether income supports are adequate for keeping people from experiencing homelessness. The Homeless Income Cut Off (HICO) measures the amount of income families and individuals need to minimize their risk of becoming homeless after they have exhausted their own efforts to remain housed. This paper explains how the HICO is calculated and how it can be used to gauge the adequacy of income support policies intended to keep Canadians from becoming homeless. Since income supports and the costs of necessities vary across Canada and over time, this paper presents calculations of the HICO for seven cities (Vancouver, Edmonton, Calgary, Winnipeg, Toronto, Montreal and Halifax) and for eight years (2015–22). The HICO does not describe what people with low-income must do before they receive income assistance. Rather, it shows how bad the affordability crisis is for them — a crisis caused by rising housing costs and food prices, lagging incomes and holes in the social safety net. The calculations show the extent to which Canadians with low income must rely on food banks and other charities to hang on to their housing, a reliance made necessary by income supports that are frequently inadequate for this purpose. The HICO provides policymakers with a gauge against which they can evaluate the adequacy on income supports for preventing homelessness. Income supports need, at the very least, to be sufficient to enable families and individuals to maintain their housing after they have exhausted their own efforts to do so.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".