Evaluating the recreational fishery management toolbox: Charter captains’ perceptions of harvest controls, limited access, and quota leasing in the guided halibut fishing sector in Alaska
Bibliographic record
Abstract
Examining the reasons why individuals choose to participate or comply with certain fishing regulations is a key part of successful fisheries management. This paper presents a case study that evaluates fisher perceptions of multiple recreational fishery regulations, including traditionally used methods of bag and size limits and a novel regulation involving quota leasing, in the for-hire (i.e., charter) recreational fishing sector for Pacific halibut (Hippoglossus stenolepis) in Alaska. This study examined responses from open-ended and Likert-scale questions from semi-structured interviews with 45 charter operators in Homer and Sitka. Our results highlight that controls on individual harvest can be perceived to have unintended consequences for charter businesses, such as effects on profitability and distance traveled. In response to open-ended questions on a voluntary quota leasing program, participants discussed themes of inequity reflecting broader perceptions of conflicts with the commercial sector and the management system. Perceived inequities that have not been fully addressed can shape how stakeholders feel about current management institutions and affect compliance. Therefore, it is important to understand the historical and political contexts of fishery systems to better anticipate support for future management approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".