Supporting Inuit food security: A synthesis of initiatives in the Inuvialuit Settlement Region, Northwest Territories
Bibliographic record
Abstract
Food insecurity among Indigenous Peoples of northern Canada is a significant public health issue that is exacerbated by changing social and environmental conditions. While a patchwork of programs, strategies and polices exist, the extent to which they address all “pillars” of food security (food availability, access, quality, and utilization) remains under-assessed. We respond to this gap by providing a framework for synthesizing and assessing information about food security initiatives, using a case study of the Inuvialuit Settlement Region (ISR), the westernmost Inuit region of Canada. Our objectives are: (1) to identify existing initiatives in the ISR; (2) to assess the breadth and diversity of these initiatives in addressing the four key food security “pillars”; and (3) to present an analytical framework that will facilitate ongoing data updating and sharing in the ISR and elsewhere. Through a scoping review and direct consultation with 12 key informants, we identified 30 initiatives that support food security in the ISR. These are funded and implemented at a range of national, territorial, regional, and local levels, and include both governmental and non-governmental programs, strategic frameworks, and research and monitoring initiatives. Seven key themes emerged from the cross-scale analysis of these initiatives, including: orientation with respect to food security pillars, scope and scale, demographic targeting, funding, monitoring and evaluation, and implications for food security strategies. While our framework provides a useful tool for data synthesis and analysis, its outputs can help in identifying gaps and opportunities for both resource allocation and program and policy development for under-served communities. Significantly, this study highlights the importance of engaging local perspectives in the development of coordinated approaches to address Inuit 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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.025 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".