Introducing Disciplinary Perspectives and Interdisciplinary Possibilities for Understanding Recreational Fishers and Fisheries
Bibliographic record
Abstract
Abstract Individuals from many disciplines conduct research to understand the social dimension of recreational fisheries. This diverse inquiry has produced a comprehensive understanding of the behaviours of recreational fishers, the outcomes from fishing, and the relationships among fishers, others, and the natural and human environment. The associated body of research, however, is largely disconnected across disciplines. Our goals here are to help readers understand the similarities and differences among disciplines and to identify opportunities for interdisciplinary-based research on recreational fishers. Before addressing these goals, we begin by answering basic questions: What is a discipline? What are the primary disciplines used to study recreational fishers? And what is the genealogy of research on recreational fishers? Seven key disciplines are then classified by multi-criteria related to both focus and epistemological and methodological approaches. This classification reveals clear connections among: (i) ecological science and resource economics, and to a lesser extent historical ecology; (ii) environmental history and political ecology; and (iii) behavioural economics and social psychology. We next describe and provide possible remedies for barriers that inhibit interdisciplinary research, including different epistemologies, nomenclature, and reward bias. Finally, we highlight opportunities to conduct interdisciplinary research by describing the types of benefits that each discipline can provide when conducting interdisciplinary research on recreational 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".