Management Effectiveness Evaluation using the Bayesian Belief Network Approach: A case study of Cu Lao Cham Marine Protected Area, Vietnam
Bibliographic record
Abstract
Abstract The Kunming-Montreal Global Biodiversity Framework set an ambitious target of safeguarding 30% of the Earth’s land and sea through well-managed Protected Areas (PAs) by 2030. Despite 196 countries committing to expand PA coverage, a lack of evidence on existing PA effectiveness and limited data on human-ecosystem interactions pose formidable challenges to implementation and prioritising conservation funding. Within this context, this paper aims to test a simple and user-friendly model, which is based on Bayesian Belief Networks, in evaluating the management effectiveness of Cu Lao Cham Marine Protected Area (CLC-MPA) in Vietnam. It focuses on assessing whether the MPA is effective in achieving its conservation objectives and if the target resources can endure the current pace of economic development. The study underscores the importance of prioritising eco-tourism and sustainable local economic models amid the intense forces of mass tourism development. It argues that tourism carrying capacity for CLC should be estimated at the outset of sustainable tourism development plan. Besides, there is a need for an integrated approach to MPA management to include legal provisions to address the impacts of tourism and pollution. Also, given the increasing pressure from incoming fishers, it is essential for increasing trans-provincial coordination for better enforcement of MPA regulations.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".