Social Factors Affecting Sustainable Shark Conservation and Management in Belize
Bibliographic record
Abstract
Predatory sharks contribute to healthy coral reef ecosystems; however their populations are declining. This paper explores some of the important social factors affecting shark conservation outcomes in Belize through a qualitative analysis of the shark-related activities, attitudes and perceptions among local stakeholders and their perceived relative ability to influence shark conservation policies. Drawing on key informant interviews and focus groups, respondents suggested that considerable demand for shark meat originates from markets in Mexico, Guatemala and Honduras, especially during Lent, driving larger-scale shark fishing operations within Belize waters. Different stakeholders reported a wide range of uses for shark products, and reported diverging perceptions concerning the status and value of shark populations in Belize, with conflicting attitudes towards their conservation. Such conflicting perceptions among stakeholders can pose a serious challenge to sustainable shark conservation and management, and ultimately undermine collaborative governance objectives. Belize shark conservation issues likely need to be addressed at the scale of the Mesoamerican Barrier Reef, perhaps by taking a transboundary approach that better accounts for the roles and responsibilities of stakeholders from Belize, Mexico, Guatemala and Honduras.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".