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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".