Canada’s northern food subsidy <i>Nutrition North Canada</i>: a comprehensive program evaluation
Bibliographic record
Abstract
BACKGROUND: Nutrition North Canada (NNC) is a retail subsidy program implemented in 2012 and designed to reduce the cost of nutritious food for residents living in Canada's remote, northern communities. The present study evaluates the extent to which NNC provides access to perishable, nutritious food for residents of remote northern communities. DESIGN: Program documents, including fiscal and food cost reports for the period 2011-2015, retailer compliance reports, audits of the program, and the program's performance measurement strategy are examined for evidence that the subsidy is meeting its objectives in a manner both comprehensive and equitable across regions and communities. RESULTS: NNC lacks price caps or other means of ensuring food is affordable and equitably priced in communities. Gaps in food cost reporting constrain the program's accountability. From 2011-15, no adjustments were made to community eligibility, subsidy rates, or the list of eligible foods in response to information provided by community members, critics, the Auditor General of Canada, and the program's own Advisory Board. Measures to increase program accountability, such as increasing subsidy information on point-of-sale receipts, make NNC more visible but do nothing to address underlying accountability issues Conclusions: The current structure and regulatory framework of NNC are insufficient to ensure the program meets its goal. Both the volume and cost of nutritious food delivered to communities is highly variable and dependent on factors such as retailers' pricing practices, over which the program has no control. It may be necessary to consider alternative forms of policy in order to produce sustainable improvements to food security in remote, northern communities.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".