Identifying Barriers and Pathways Linking Fish and Seafood to Food Security in Inuit Nunangat: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Fish and seafood play an important role in improving food security in Inuit Nunangat. Therefore, this scoping review aims to explore (1) what topics and/or themes have been widely explored in the literature related to barriers and pathways linking fish and seafood to food security; (2) where research, policy, and action gaps exist; and (3) how fisheries currently contribute to food security. METHODS: A systematic search of peer-reviewed articles was conducted using six databases. Articles were screened by two independent reviewers. Eligible studies included primary research conducted in Inuit Nunangat that explored the roles of fish and seafood in food security. RESULTS: Thirty-one articles were included for review. Overall, we found that fisheries can influence food security through direct pathways (e.g., consuming fish for nutrition), and through indirect pathways such as increasing household purchasing power (e.g., through employment). Research indicated that policies relating to wildlife and fisheries management need to be integrated with food and health policies to better address food insecurity in Inuit Nunangat. CONCLUSION: Future research is needed to establish a more robust understanding of the explicit mechanisms that fish and seafood harvest and/or the participation in commercial fisheries alleviates household 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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".