No fear of bankruptcy: the innate self-subsidizing forces in recreational fishing
Bibliographic record
Abstract
Abstract Recreational fishing, by both local residents and tourists, is a popular activity globally. The behaviour and motivation of recreational fishers is different from those of commercial fishers. Unlike the latter, the former are not dependent on making profits to continue fishing. Rather, the value of recreational fishing to those who engage in it is a combination of catches and experience values. The latter value implies that recreational fishers might continue fishing when they should not, analogous to the effect of subsidy in the commercial fishing sector. Hence, the term “self-subsidizing”: a fishery as one in which fishers subsidize themselves through an economic investment in gear and time from their non-fishery-based earnings. The consequence of which is that recreational fishers can continue fishing long after the commercial fishing industry has stopped fishing because their operations have become economically unviable. There is reason to argue that in many areas, recreational fishing effort, due to the self-subsidizing mechanism, is sustained at a high rate while stocks decrease. In this contribution, we describe the innate self-subsidizing forces in recreational fishing and discuss their implications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".