How do human actions affect fisheries? Differences in perceptions between fishers and scientists in the Maine lobster fishery
Bibliographic record
Abstract
The degree to which human actions affect marine fisheries has been a fundamental question shaping people’s relationship with the sea. Today, divergences in stakeholder views about the impacts of human activities such as fishing, climate change, pollution, and resource management can hinder effective co-management and adaptation. Here, we used surveys to construct mental models of the Maine lobster fishery, identifying divergent views held by two key stakeholder groups: lobster fishers and marine scientists. The two groups were differentiated by their perceptions of the relative impact of pollution, water temperature, and fishing. Notably, many fishers perceive the process of fishing to have a positive effect on fisheries through the input of bait. Scientists exhibited a statistically significantly stronger concern for climate change and identified CO2 as one of the dominant pollutants in the Gulf of Maine. However, fishers and scientists agreed that management has a positive impact, which appeared to be a change over the past two decades, possibly due to increased collaboration between the two groups. This work contributes to the goal of decreasing the distance between stakeholder perspectives in the context of a co-managed fishery as well as understanding broader perceptions of impacts of human activities on marine ecosystems.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".