Inuit Food Insecurity as a Consequence of Fragmented Marine Resource Management Policies? Emerging Lessons from Nunatsiavut
Bibliographic record
Abstract
Historically, Inuit communities of the Arctic have relied significantly on the living marine resources of their coastal waters for nutrition, underpinning community cohesion and enhancing individual and collective well-being. Inadequate understanding of the conditions of coastal marine stocks and their dynamics, along with failed past fisheries management practices, now threatens secure access to these resources for food and nutrition. We examine the degree of integration of modern Canadian federal food and marine resource management policies, which heretofore have been unable to lessen food insecurity in the Arctic, suggesting that causes rather than symptoms need to be treated. Using evidence from Nunatsiavut, northern Labrador, we assess the limits to marine resource governance affecting access to traditionally important food sources. We explore the potential for both increased subsistence harvests and enhanced access to commercial fisheries in mitigating Inuit food insecurity, arguing for the relevance of expanded marine resource assessments, more focused fisheries management, and integration with policies designed to mitigate food insecurity. Crucially, the absence of methods for tracking changes in locally harvested marine resources threatens not only individual and household nutrition but also the social, economic, and cultural integrity of Inuit communities. We further describe the needs for monitoring and propose the use of indicators that capture the contributions of locally harvested marine resources to increased food security along with a framework that allows for utilizing local knowledge and observations. Relying on emerging lessons from research in Nunatsiavut, we build a foundation for a better understanding of both the political and institutional legacies that contribute to Labrador Inuit food insecurity and discuss how the deeper integration of food and marine resource management policies could help mitigate it.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".