Housing affordability and the adequacy of household food expenditures among Canadian households
Bibliographic record
Abstract
Research suggests that housing circumstances may underpin the vulnerability of some low‐income households to food insecurity. To explore the relationship between housing affordability and food access, data from the 2001 Survey of Household Spending (n = 16,091), a nationally‐representative Canadian expenditure survey, was analyzed. The adequacy of food spending was assessed in relation to the cost of the Nutritious Food Basket (NFB), which represents a basic nutritional diet. As the proportion of household income allocated to shelter increased, the ratio of food spending to the cost of the NFB declined significantly. As housing consumed more than 30% of income, the ratio fell below 1, affirming at a population level the notion of affordable housing as that which consumes 30% or less of income. Stratifying households by a categorical income variable indicated more pronounced inverse relationships between share of income consumed by shelter and adequacy of food spending as income declined. Interestingly, the receipt of housing subsidies (a response to affordability problems typically functioning to fix rent at 30% of income) was not associated with higher adequacy of food spending among low‐income households. Our findings highlight affordable housing as a potential policy lever to alleviate problems of food insecurity among low‐income households, but suggest that current interventions are inadequate.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".