Bibliographic record
Abstract
This paper considers the complexities involved in the co-management of marine protected areas involving local NGOs.Based on field research in Belize, it examines the interactions between local NGOs involved in the MPA co-management process with state agencies, and local users, focusing on fishers and tour guides.These interactions highlight the fact that the interests of local NGOs and the resource users they represent are not always convergent.Yet the challenges of negotiating the shifting and, at times, divergent interests are ones for which NGOs are often not prepared to meet.This paper argues that, in tandem with training in strategic planning and proposal writing, negotiation and conflict mediation skills are crucial to the success of building positive relations between local NGOs and community stakeholders and, in turn, to fostering long-term success in the co-management process.
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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 | 0.042 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.049 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".