Technological innovations in the recreational fishing sector: implications for fisheries management and policy
Bibliographic record
Abstract
Technology that is developed for or adopted by the recreational fisheries sector (e.g., anglers and the recreational fishing industry) has led to rapid and dramatic changes in how recreational anglers interact with fisheries resources. From improvements in finding and catching fish to emulating their natural prey and accessing previously inaccessible waters, to anglers sharing their exploits with others, technology is completely changing all aspects of recreational fishing. These innovations would superficially be viewed as positive from the perspective of the angler (aside from the financial cost of purchasing some technologies), yet for the fisheries manager and policy maker, technology may create unintended challenges that lead to reactionary or even ill-defined approaches as they attempt to keep up with these changes. The goal of this paper is to consider how innovations in recreational fishing are changing the way that anglers interact with fish, and thus how recreational fisheries management is undertaken. We use a combination of structured reviews and expert analyses combined with descriptive case studies to highlight the many ways that technology is influencing recreational fishing practice, and, relatedly, what it means for changing how fisheries and/or these technologies need to be managed-from changes in fish capture, to fish handling, to how anglers share information with each other and with managers. Given that technology is continually evolving, we hope that the examples provided here lead to more and better monitoring of technological innovations and engagement by the management and policy authorities with the recreational fishing sector. Doing so will ensure that management actions related to emerging and evolving recreational fishing technology are more proactive than reactive.
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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 | 0.034 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| 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".