The political ecology of gear bans in two fisheries: Florida's net ban and Alaska's Salmon wars
Bibliographic record
Abstract
Abstract Parametric management in fisheries, which describes the management of how, where and when fishing occurs, is often essential for achieving sustainability. Changes to these parameters likely have impacts on stakeholders, however, for example through the costs and allocative consequences of spatial restrictions or gear changes. Here, I discuss two cases where gear bans have been implemented or proposed in response to conservation concerns: the commercial net ban enacted in Florida in 1995 and the failed ban on set gill‐nets in parts of Alaska. The two cases are remarkably similar, although the outcomes were quite different because of the social context of each fishery. Lessons from the Florida ban, which resulted in numerous negative social and ecological impacts, are informative regarding the impacts that likely would have accompanied the Alaska ban, had it proceeded. In both cases, the gear bans have had or were poised to have notable impacts on allocation, but scientific evidence for their necessity was limited. These cases show how ethical considerations can be inseparable from the ecological aspects of managing fisheries, and that when communities grapple with the sustainability of fisheries, they are simultaneously seeking to define the socially acceptable uses of those resources. I suggest a set of questions that can be asked when proposing parametric changes to fisheries, including how those changes will impact social well‐being and community resilience. These are questions that I argue must be addressed if both ethical and sustainable fisheries are the goal.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".