Interactive Governance for the Sustainability of Marine and Coastal Resources in Thailand
Bibliographic record
Abstract
Coastal zones are biodiverse, with complex and dynamic interconnectivity between terrestrial and marine areas, and with multiple interactions between ecological and social systems. Despite on-going efforts to conserve and protect these ecosystems, destructive extraction and unsustainable resource utilization are persistent, posing challenges for governance. Issues and concerns in coastal zones are cross-sectoral and cross-boundary, often with overlapping jurisdictions. They are considered ‘wicked’ governance problems, requiring nuanced approaches to address, rather than technical quick fixes. Interactive governance is one such approach that examines relationships within and between the ecological and social systems, as well as with the governing system. Theoretically, the governability of coastal zones depends on the inherent quality of these systems and their interactions, and improving governability needs to take place in all three orders of governance. At the ‘first order’, a better understanding of the diversity, complexity and dynamics of coastal zones, and related scale issues is required. Improving governability at the ‘second order’ involves evaluating and adjusting the existing legal and institutional frameworks to improve the performance and the correspondence with the systems they aim to govern. Finally, discussion about coastal governance needs to be elevated to ‘meta-order’ where principles are set and values derived so that hard choices can be made, for instance, between conservation and utilization of coastal resources. Guided by the interactive governance framework, the paper presents an overview of coastal governance in Thailand, summarizing key features of the natural, social and governing systems associated with coastal zones, and discussing what can be done to improve coastal governability.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".