BUILDING A SYNERGISTIC MODEL ON CHEMICAL AND WASTE MULTILATERAL ENVIRONMENTAL AGREEMENTS TO IMPROVE ENVIRONMENTAL ENFORCEMENT : A CASE STUDY OF MULTILATERAL ENVIRONMENTAL AGREEMENTS REGIONAL ENFORCEMENT NETWORK
Bibliographic record
Abstract
Proliferation of multilateral environmental agreements (MEAs) leads to institutional fragmentation, duplication as well as overloading the national administration and likely causes ineffectiveness of MEAs implementation. Using collective action theory, inter-organization theory and propositions on synergy, clustering, fragmentation and regime effectiveness, this research closely examined a case of MEA Regional Enforcement Network (MEA REN), a pilot project aimed at strengthening enforcement of four chemical and waste related MEAs (Basel/Rotterdam/Stockholm Conventions and Montreal Protocol) in Asia, to prove the claim that building MEAs synergies would improve enforcement effectiveness. The study was conducted through in-depth interview, documentation review, comparing trade data, and qualification analysis. The study concluded that synergy building could improve information flows, inter-agency cooperation, law enforcement operations, capacity building and enforcing licensing system so that countries can enforce MEAs in a more effective way. The study recommended organization reform, enforcement networking and capacity building are key areas to improve enforcement effectiveness, and constructed a model of building synergies for chemical and waste related MEAs to improve environmental enforcement.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".