Economic Experiments in Recreational Fishing Studies
Bibliographic record
Abstract
Abstract Experimentation in natural science is commonplace, and the reasons and methods for undertaking it are well understood. The use of experimental methods has risen remarkably over the last 25 years in the field of economics and has had a tremendous impact on the way economic research is carried out. This raises the question; how can economic experiments advance our understanding of recreational fishing. Here, we argue that many opportunities and benefits exist for the application. There have already been several successful experiments (in the lab or the field) to understand the drivers of recreational fisher behaviour. Despite these efforts, there remains a raft of opportunities to use experiments to help understand cause and effect in terms of recreational fisher behaviour. In this chapter, we provide a taxonomy of economic experiments and highlight their application to recreational fisheries. We sketch how controlled and uncontrolled interventions in recreational fisheries are learning opportunities to better understand fisher behaviour and social-ecological dynamics. Further, we highlight practical aspects of experimentation that will help its implementation and suggest opportunities for future experiments in recreational fisheries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".