Voluntary institutions and behaviours as alternatives to formal regulations in recreational fisheries management
Bibliographic record
Abstract
Abstract Traditional regulatory options (formal institutions) imposed by government agencies such as harvest and gear restrictions represent the standard in recreational fisheries management, at least in developed countries. However, there exist a number of alternatives including the use of angler education programmes that attempt to evoke voluntary changes in angler behaviour, resulting in the emergence of voluntarily motivated resource‐conserving informal institutions. These ‘softer’ approaches to aquatic stewardship and fisheries management can be developed in cooperation with stakeholders and in many cases are led by avid anglers and angling groups. Examples of such measures include voluntary sanctuaries, informally enforced seasonal closures, personal daily bag limits, self‐imposed constraints on gear, development of entirely live‐release fisheries, and adoption of fish and aquatic ecosystem conservation‐oriented gears and release practices. Education efforts that provide anglers with knowledge on best practices and empower them to modify their behaviour hold great promise to meet formal management goals and objectives, but seem to be underutilized relative to formal regulations. This article highlights the benefits and challenges of relying on informal institutions as alternatives to traditional regulatory options. Informal institutions that protect resources and help overfished stocks recover hold great promise in both developed and developing countries, particularly when there is a single stakeholder group or when the capacity to enforce traditional regulations or to invest in stock assessments is limited. Informal institutions may help make formal institutions more effective or can even be alternatives to costly institutions that depend on enforcement to be effective.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".