Food Security and Food Sovereignty Initiatives in Inuit Nunangat : Preliminary Results of a Scoping Study
Bibliographic record
Abstract
The present report consists of a preliminary survey of food programs and funding sources in Inuit Nunangat, conducted during Summer 2023 by three master’s students, Lucie Cordier, Hubert Demers-Campeau, and Laurie Hétu, under the supervision of Professors Nathan McClintock and Magalie Quintal-Marineau of the Institut national de la recherche scientifique (INRS). This survey forms the basis of a scoping analysis of food programs, the first phase of a larger SSHRCfunded research project led by professors McClintock and Quintal-Marineau entitled “Scoping and Storying Food Governance in Inuit Nunangat”, which examines food sovereignty in Inuit Nunangat, with particular attention to Nunavik. The goal of the scoping analysis is to identify and characterize the various actors and institutions involved in improving food security in Inuit Nunangat, the types of practices these programs promote and support, as well as the language (discourse) used by the different programs and funders. Specifically, our first goal with this preliminary report is to characterize the diversity of food security programs. What are their primary areas of focus? Where do they receive their funding? Who manages them? A second goal is to begin to understand the extent to which food programs in Inuit Nunangat support or promote Inuit food sovereignty and self-determination. To this end, in the report we also present our preliminary efforts to trace the evolution of food sovereignty discourse in Inuit Nunangat and assess the extent to which food initiatives in the North are led or managed by Inuit and support the harvesting of country food. We begin with an overview of the evolution and adoption of the food sovereignty framework in Inuit Nunangat. We then present the preliminary results of our scoping analysis of 80 food programs and 56 funding sources focused on some aspect of improving food security in Inuit Nunangat. In our analysis, we examine the central focus of these programs (e.g., community development, education, emergency food) and their funding source (e.g., federal or regional government). As a proxy measure of the contribution of these initiatives to Inuit food sovereignty, we also assess whether they are Indigenous-led or managed and whether they explicitly address country food. It is important to note that these results are preliminary, as the collection and analysis of initiatives is ongoing. After presenting the results of our scoping analysis, we present three examples of food sovereignty initiatives in Inuit Nunangat. We conclude with a discussion of next steps.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".