Labrador Inuit resilience and resurgence: embedding Indigenous values in commercial fisheries governance
Bibliographic record
Abstract
Increasingly there is recognition of the need for new governance and decision-making models in natural resource management that uphold the rights and knowledge systems of Indigenous peoples. These models would support access to and sovereignty over natural resources including fisheries and wild harvested foods. However, research in northern Indigenous communities continually focuses on country foods and subsistence harvests and does not consider the important role of commercial fisheries. It is key to investigate how Inuit cultures and commercial fisheries are linked to understand how fisheries governance should be directed. Through an iterative interview process, we identify values and principles held by Labrador Inuit fishers and fisheries managers regarding the commercial fishing industry, outlining an interconnected set of values that ground how Labrador Inuit relate to the fisheries today. Drawing on the literature, we contrast the current fisheries management paradigm with the values that arise from this study. By identifying and articulating a system of values held by Labrador Inuit in relation to the commercial fishing industry, we articulate a set of principles to inform a desirable and just future for commercial fisheries. This represents a new conceptual model for Inuit commercial fisheries, one that speaks to the resilience of Labrador Inuit, and frames the industry as having value beyond its material dimensions, to include political self-determination, traditional use, and cultural identity.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| 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".