Human dimensions of ecosystem-based management: Lessons in managing trade-offs from the Northern Shrimp Fishery in Northern Peninsula, Newfoundland
Bibliographic record
Abstract
Fisheries can have significant impacts on the structure and function of marine ecosystems, including impacts on habitats and non-target species. As a result, management agencies face growing calls to account for the ecosystem impacts of fishing, while navigating the political and economic interests of diverse stakeholders. This paper assesses the impacts of two specific factors on the attitudes and well-being of shrimp fishers in the context of a selective fisheries closure designed to protect crabs in the Northern Peninsula of Newfoundland and Labrador, Canada: (1) the species portfolios of fishers; and (2) democratic rulemaking. The results of this analysis suggest that shrimp fishers were more likely to support selective closures for the shrimp fishery if they also fished for crab, and felt they had an influence on the management of the fishery. The results further indicate that species portfolio diversification had a positive and statistically significant impact on the subjective economic well-being of fishers. This study contributes to an emerging literature on the human dimensions of ecosystem based fisheries management, highlighting opportunities to address trade-offs in fisheries through species diversification and by enhancing the role and influence of fishers in management processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".