Bibliographic record
Abstract
The article begins with a sketch of the main theoretical perspectives on compliance with multilateral environmental agreements (MEAs). Much of the theoretical debate in the context of MEAs has focused on the respective merits of the “managerial” and “enforcement” approaches. Both approaches can be situated within rational institutionalism. Relatively less attention has been paid in the MEA debate to explicitly constructivist frameworks, and to efforts to identify the features that enable legal norms to exert distinctive influence on actors. Such approaches enrich the debate and provide important additional insights into ways to promote compliance with MEAs. The article then surveys the emergence and key features of MEA-specific non-compliance procedures. Next, it provides an overview of those features of the Kyoto Protocol to the UN Framework Convention on Climate Change that are directly relevant to the design of a compliance regime. Against this backdrop, the article outlines the main elements and innovations of the Procedures and Mechanisms on Compliance under the Kyoto Protocol that were adopted at Marrakech in November 2001. The article concludes with an assessment of the Procedures and Mechanisms, highlighting apparent trends in the design of MEA compliance regimes and their import for the theoretical debate on compliance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.008 | 0.024 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".