Recreational angler and stakeholder perceptions of policy changes to recreational fishery management: the case of red snapper devolution in the Gulf of Mexico
Bibliographic record
Abstract
In 2020, management of a prized recreational sportfish species, Lutjanus campechanus (red snapper), underwent a landmark change in the Gulf of Mexico of the United States: from federal management at the national level to a more localized, state level management. This policy change is based on the idea that localized management, informed by greater understanding of the context of the social-ecological system, enhances resilience. But how do fisheries stakeholders see this policy change? Our research asks how fishery stakeholders’ perceptions, especially recreational fishermen’s, vary across those who supported and did not support this change in management, known as Amendment 50 to the Gulf of Mexico Reef Fish Management Plan. We analyzed n = 2206 stakeholder comments using mixed methods and qualitative coding. Our thematic analysis found that 40% of comments supported devolved state management, 3% opposed it, and 57% could not be classified because the comment did not explicitly state support or opposition. In this paper, we only analyze the comments that explicitly support (40%) or oppose (3%) state management. We found that supporters of more localized fisheries management believe that it is characterized by (1) greater flexibility in management, (2) more trustworthiness, (3) better recreational access, and (4) trustworthy science. We argue that these four beliefs, analyzed through inductive methods, form a locally accepted and context-dependent model of resilient management for one of the most iconic recreational fisheries in the United States, in one of its fastest growing coastal regions. This model, built from these four beliefs, are connected by stakeholder trust in government. Understanding how to enhance stakeholder, especially recreational fishermen, trust in government has important implications for sectors beyond fishery management.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".