Adaptive Mechanisms for Addressing Coastal Erosion Through Environmental Education: A Case Study of Samut Sakhon Province, Thailand
Bibliographic record
Abstract
Coastal erosion poses a severe and growing threat to shoreline communities in Thailand, particularly in Samut Sakhon Province, where socioeconomic vulnerability and environmental degradation intersect. In this study we employ a mixed-methods approach to examine the mechanisms of community-based adaptation through the lens of the environmental education process (EEP). The approach integrates qualitative interviews (n = 85), quantitative surveys (n = 364), and spatial vulnerability mapping. The findings reveal significant economic insecurity, low levels of community participation in environmental organizations, and limited knowledge and preventive behavior regarding coastal erosion. Statistical analysis indicates a strong correlation between adaptive behavior and various factors, including knowledge, attitudes, community participation, access to information, and land use. Knowledge emerges as the strongest predictor of adaptive behavior (β = .389, p < .001). These insights form the basis of a participatory adaptation model that connects local knowledge systems with nature-based solutions and environmental learning frameworks. The study emphasizes the significance of integrating education, participatory governance, and ecosystem restoration to enhance coastal resilience. The proposed model serves as a scalable foundation for policy innovation and sustainable shoreline management in vulnerable coastal regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".