Effects of marine management measures on saltwater recreational fishers in British Columbia, Canada: Fishers’ motivations, values, beliefs, and marine stewardship
Bibliographic record
Abstract
ABSTRACT Objectives Marine recreational fishing provides social, cultural, nutritional, and economic benefits, which can be impacted by marine management measures. Understanding fishers’ motivations and fishing-related activities and how they are impacted by management measures can help managers and decision makers. This type of research addresses the human dimensions of recreational fishing. Methods To understand potential impacts, we (representing a public university and a local nonprofit fishing society) developed an online survey (n = 1,918 responses) to assess fishers’ activities, motivations, beliefs about management, perceived impacts of management measures, and involvement in marine stewardship and citizen science in British Columbia, Canada. Results Fishers were motivated by time spent outdoors with family and friends, keeping fish, and mental and physical health benefits (the latter becoming more important during the COVID-19 pandemic). Rules and regulations that did not allow retention were equated with no opportunity. Survey respondents agreed that management measures were necessary, but they disagreed with many current measures and felt that their needs and concerns were not considered. Many survey participants were involved in stewardship and citizen science, especially those working in the service sector (e.g., guides). Conclusions Our results emphasize the importance of improving trust in and the legitimacy of fisheries management decision making, such as outreach by fisheries managers and implementation of collaborative governance systems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".