From the Food Mail Program to Nutrition North Canada: The impact on food insecurity among Indigenous and non‐Indigenous families with children
Bibliographic record
Abstract
Abstract Food insecurity is prevalent in northern Canada, especially among Indigenous peoples. As one approach to address this issue, the federal government subsidizes the shipping of necessities to remote northern communities, initially through the Food Mail Program and then Nutrition North Canada as of April 2011. We use the Canadian Community Health Survey (2007 to 2016) and a difference‐in‐differences model to estimate the impact of the policy change on food insecurity, testing for heterogeneity between Indigenous and non‐Indigenous families. Our results, which withstand several robustness checks, indicate that the policy change increased the likelihood of overall food insecurity by 8.9 percentage points (77.3% relative to the sample mean) and moderate/severe food insecurity by 7.1 percentage points (89.3% relative to the sample mean). It also increased severe food insecurity among Indigenous families by 7.3 percentage points (more than three times the sample mean). There was, however, variation across regions and subsamples of families with children. Specifically, the policy change was particularly harmful to Indigenous families in the territories and Inuit Nunangat. The detrimental impact was also heightened in the presence of children, especially when considering severe food insecurity among Indigenous families.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".