Moving beyond panaceas in fisheries governance
Bibliographic record
Abstract
In fisheries management-as in environmental governance more generally-regulatory arrangements that are thought to be helpful in some contexts frequently become panaceas or, in other words, simple formulaic policy prescriptions believed to solve a given problem in a wide range of contexts, regardless of their actual consequences. When this happens, management is likely to fail, and negative side effects are common. We focus on the case of individual transferable quotas to explore the panacea mindset, a set of factors that promote the spread and persistence of panaceas. These include conceptual narratives that make easy answers like panaceas seem plausible, power disconnects that create vested interests in panaceas, and heuristics and biases that prevent people from accurately assessing panaceas. Analysts have suggested many approaches to avoiding panaceas, but most fail to conquer the underlying panacea mindset. Here, we suggest the codevelopment of an institutional diagnostics toolkit to distill the vast amount of information on fisheries governance into an easily accessible, open, on-line database of checklists, case studies, and related resources. Toolkits like this could be used in many governance settings to challenge users' understandings of a policy's impacts and help them develop solutions better tailored to their particular context. They would not replace the more comprehensive approaches found in the literature but would rather be an intermediate step away from the problem of panaceas.
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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 | 0.056 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.027 | 0.080 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".