The practice of compliance and implementation in multilateral environmental agreements
Bibliographic record
Abstract
Ever since a compliance and implementation mechanism (CIM) was incorporated into the 1987 Montreal Protocol, there has been a proliferation of such mechanisms within multilateral environmental agreements (MEAs). Given the complex nature of environmental problems, CIMs have proven to be a better means by which to manage MEA compliance and implementation compared to traditional dispute settlement mechanisms. While the design of CIMs varies from one MEA to another, they tend to include three core functions, namely reporting, reviewing and addressing weak implementation or non-compliance. In terms of reporting, MEAs include measures by which parties usually self-report on their progress in implementing an MEA, although some have options for non-parties to report or comment on the reports submitted by parties. Reviewing these reports is often left to the secretariat of the MEA. Additionally, compliance and implementation committees often play a key role in reviewing incidences of non-compliance or weak compliance. There are various ways in which such a committee may hear a case, including through self-reporting by a party itself, by one party raising an issue of another party, or through a ‘committee initiative’, i.e., where information is provided to the committee from other sources, such as from non-governmental organisations. In terms of addressing weak implementation or non-compliance in MEAs, the provision of financial and technical assistance might be the most effective means by which a party that is struggling to implement its commitments is supported.
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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 | 0.257 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.011 | 0.056 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".