Taking fishers’ knowledge and its implications to fisheries policy seriously
Bibliographic record
Abstract
Sustainable fishing is one of the most pressing challenges for mankind and requires insightful knowledge of the drivers that may foster or hinder predatory exploitation. It has been widely recognized that Indigenous and local knowledge can contribute to biodiversity conservation and sustainable use of resources, such as fisheries, worldwide. Nevertheless such knowledge continues to be marginalized and unacknowledged by a range of academic scientists and policy makers. In the present paper, we tackle this issue by discussing laws regarding closed fishing seasons, which are part of the Brazilian environmental policies for protecting marine fauna, from the perspective of artisanal fishers’ knowledge. In Brazil, these laws are typically based on governmental decisions (i.e., by administrative organizations and researchers acting as consultants) without taking fishers’ knowledge into account. Through semi-structured interviews with traditional experts of fishing villages situated along the northeast coast of Brazil, we aimed to investigate their knowledge of fish reproductive periods and analyze how it is related to the closed seasons at work in their region. We found an exact agreement between fishers’ knowledge and closed season regulations on the reproductive period of the mangrove crab (Ucides cordatus), but a conflict regarding the reproductive period of two snook species and four species of shrimps. We highlight the potential of fishers’ knowledge contributions to environmental regulations and we also explore three challenges of incorporating epistemic diversity in environmental policy. We conclude by advocating for a reflexive transdisciplinarity that highlights the potential of Indigenous and local knowledge while critically reflecting on the methodological and political challenges of transdisciplinary practices.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| 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".