Political Ecology for Understanding Recreational Fishers and Fisheries
Bibliographic record
Abstract
Abstract Political ecology, and its core concern with how power shapes social–ecological relationships, has much to offer recreational fisheries analyses. Political ecologists bring critical questions about how different fishers may have uneven access to resources, how particular policy narratives affect fishers, and how fishing communities are entangled with broader social, economic, and ecological processes. We explain the origins, key theoretical tenets, and methodological approaches of the field, describe the ways political ecology theory has been applied to understand recreational fisheries, and explore ways it could be applied in future research and management. With its focus on making visible the often hidden, power-laden relationships that shape the character of recreational fishing in specific places, political ecology investigations reveal the ways recreational fishing grapples with its own role in shifting ecologies and is also interwoven with resource-use conflicts and political movements. We bring our diverse perspectives as academics and fisheries managers to illustrate key moments when the central themes of political ecology have helped us to better understand recreational fisheries dynamics. Finally, we offer a set of best practices for integrating a political ecology perspective into recreational fishing studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".