The role of provincial social policies and economic environments in shaping food insecurity among Canadian families with children
Bibliographic record
Abstract
Food insecurity, inadequate access to food due to financial constraints, affects 17.3% of Canadian children, with serious health repercussions. Capitalizing on the geo-temporal variation in social policies and economic environments across Canadian provinces between 2005 and 2018, we examined the association between provincial policies and economic environments and likelihood of experiencing food insecurity among households with children. Drawn from 13 years of the Canadian Community Health Survey, our sample comprised 123,300 households with below-median income with children under 18 in the ten provinces. We applied generalized ordered logit models on the overall sample and subsamples stratified by Low-Income Measure (LIM). Higher minimum wage, lower income tax, and lower unemployment rate were associated with lower odds of food insecurity in the overall sample. A hypothetical one-dollar increase in minimum wage was associated with 0.8 to 1.0-percentage-point decrease in probability of food insecurity. The probability of food security increased by 1.2 to 1.6 percentage points following a one-percentage-point drop in bottom-bracket income tax rate. One-percentage-point lower unemployment rate corresponded to 0.6 to 0.8-percentage-point higher probability of food security. Higher welfare income and lower housing price predicted lower likelihood of severe food insecurity in the below-LIM subsample. Higher sales tax and median wage predicted higher likelihood of food insecurity among above-LIM households. Income support policies, favorable labor market conditions, and affordable living costs were all related to reduced food insecurity among Canadian households with children. Policies that increase minimum wage, reduce taxes, and create jobs may help alleviate food insecurity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".