A Tale of Two Fisheries: Exploring Angler Behaviour that Informs Different Management and Conservation Goals
Bibliographic record
Abstract
Recreational fishing is an important activity primarily enjoyed for the purposes of pleasure or competition. Despite the many benefits of interacting with nature and harvesting wild food, recreational fishing presents a myriad of negative impacts to fish populations, thus requiring management interventions to ensure sustainability. Since management conservation measures typically involve angler compliance with regulations and voluntary adoption of proconservation behaviours, I analyzed social data from two fisheries facing contrasting conservation challenges to identify the prevalence of selfreported proconservation behaviours among recreational anglers. I further investigated the factors which influence such behaviours in an effort to dissect how certain desired behaviours may be encouraged to support management conservation goals and to contribute to knowledge surrounding angler behaviour. My results indicate high levels of participation in voluntary proconservation behaviours and may inform management strategies that would benefit from angler participation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".