Political Parties and Household Food Insecurity Among Canadian Provinces: A Panel Data Analysis, 2005–2014
Bibliographic record
Abstract
In Canada, links between social determinants and household food insecurity (HFI) are well-documented, but the influence of political parties remains unclear. This study examines whether political parties predict HFI rates across Canadian provinces and explores the mediating roles of low income and social assistance. Panel data from 2005 to 2014 were obtained from Statistics Canada, with political party strength categorized as left, center, or right. Linear regressions with Driscoll and Kraay standard errors reveal that left-leaning parties are associated with lower HFI rates, right-leaning parties with higher rates, and center parties show no significant effect, controlling for demographic and economic factors. Low income and social assistance fully mediate the effect of left parties but only partially mediate the effect of right parties. These findings provide insights into the politics of food insecurity, with implications for social work in the context of COVID-19.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".