Shrimp Allocation Policies and Regional Development Under Conditions of Environmental Change: Insights for Nunatsiavutimmuit
Bibliographic record
Abstract
This report is part of a larger research program examining the relationship between fisheries policy and \nregional development in Atlantic Canada’s northern shrimp fisheries. Since the extension of Canadian jurisdiction over its 200 mile Exclusive Economic Zone in 1977, federal policy makers have allocated shrimp licenses and quotas to cooperatives, community based organizations, inshore fish harvesters, large fishing companies as well as Indigenous groups. However, our knowledge of the relationship between fisheries policy and regional development outcomes in this fishery remains very limited, with the exception of case studies of a few organizations and regions in southeast Labrador and in \nNewfoundland. Despite the long history of substantial allocations of shrimp in northern Labrador/Nunatsiavut, we know little about how effective allocation policies have been in meeting regional development goals for Indigenous communities in the region. The objective of this research is to build on and extend our larger research project by identifying allocation policies that have enabled Nunatsiavut communities, and people to benefit from the shrimp fishery and to identify those \ndevelopment benefits in a systematic way. The research findings help us meet two further practical objectives: to provide research evidence to inform federal, provincial, and municipal policymaking and decision-making and to assist regional bodies and community groups in their decision-making and activities aimed at improving social, economic, cultural, and environmental conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".