Provisioning fisheries: A framework for recognizing the fuzzy boundary around commercial, subsistence, and recreational fisheries
Bibliographic record
Abstract
ABSTRACT Although sparse, increasing evidence suggests an overlooked population of fishers whose fishing motivations and outcomes overlap across commercial, subsistence and recreational fishing sectors, resulting in underrepresented groups of fishers in management and policy frameworks. These fishers participate in what we frame as “provisioning fisheries,” a concept we propose to highlight the underrepresented values from fishing and fisheries across recreational, sociocultural, psychological, economic, health, and nutritional dimensions. We argue that provisioning fisheries often support underserved groups, provisioning fishers may engage in informal markets, and, that distinction exists from sport-oriented recreational fisheries in power, risks, access barriers, fishing motivation, attitudes, and practices including rule and advisory awareness. We propose that provisioning fisheries should be consciously considered—whether as part of existing fisheries structures or even its own sector to promote more sustainable and inclusive fisheries management. Overlooking this population of fishers may risk further marginalization, conflicts, contaminant exposure, and inaccurate stock estimates. Therefore, we propose provisioning fisheries as a useful analytical category to explore the heterogeneity of fishers and their distinct needs, motivations, and behaviors. As an example of how these fisheries may function, we synthesize what we currently know about provisioning fisheries in North America with hypothesized differences between provisioning and the sport-oriented recreational fisher to encourage greater dialogue and investigation about underrecognized fisheries.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".