Can small-scale commercial and subsistence fisheries co-exist? Lessons from an indigenous community in northern Manitoba, Canada
Bibliographic record
Abstract
Subsistence (or food) fisheries are under-studied, and the interaction between subsistence and commercial fisheries have not been studied systematically. Addressing this gap is the main contribution of the present paper, which focuses on how to deal with the challenge of overlapping commercial and subsistence fisheries. The study was conducted in Norway House Cree Nation, with qualitative data collection and questionnaire surveys. Commercial fishing in Norway House takes place during spring/summer and fall seasons, whereas subsistence fishing takes place throughout the year. Commercial fishing mostly occurs in the open waters of Lake Winnipeg; subsistence fishing in rivers adjacent to the reserve and in smaller lakes inland. How do fishers and the community deal with overlaps and potential conflicts between the two kinds of fisheries? The main mechanism is the separation of the two temporally and spatially. In the remaining overlap areas, conflict resolution relies on monitoring of net ownership and informal communication. The first mechanism is regulatory but really de facto co-management in the way it is implemented. The second is consistent with Cree cultural values of respect, reciprocity and tolerance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".