Equitable Allocations in Northern Fisheries: Bridging the Divide for Labrador Inuit
Bibliographic record
Abstract
Canada has undertaken commitments to recognize the rights of Indigenous Peoples in fisheries through policies and agreements, including Integrated Fishery Management Plans, the Reconciliation Strategy, and Land Claim Agreements (LCAs). In addition to recognizing rights, these commitments were intended to respect geographic adjacency principles, to enhance the economic viability of Indigenous communities, and to be reflective of community dependence on marine resources. We examined the determinants of quota allocations in commercial fisheries involving Nunatsiavut, Northern Labrador, the first self-governing region for the Inuit peoples in Canada. It has been argued that current fishery allocations for Nunatsiavut Inuit have not satisfied federal commitments to recognize Indigenous rights. Indicators that measure equity in commercial allocations for the turbot or Greenland halibut ( Reinhardtius hippoglossoides ) and northern shrimp ( Pandalus borealis) fisheries were identified and assessed. In these two cases, historical allocations continue to predominate for allocations based upon equity or other social or economic considerations. We illustrate equity-enhancing changes in the quota distribution under scenarios of different levels of inequality aversion, and we make qualitative assessments of the effects of these allocations to Nunatsiavut for socioeconomic welfare. This approach could benefit fisheries governance in Northern Labrador, where federal commitments to equity objectives continue to be endorsed but have not yet been integrated fully into quota allocations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".