The New Generation of Environmental Non-Compliance Procedures and the Question of Legitimacy
Bibliographic record
Abstract
Non-Compliance Procedures are designed to facilitate the compliance of States parties with obligations deriving from Multilateral Environmental Agreements but may trigger harsher means to elicit compliance such as suspension of a party’s rights. The chapter will analyse the classical NCPs such as those in the Montreal Protocol on Substances That Deplete the Ozone Layer, the Convention on International Trade in Endangered Species of Wild Fauna and Flora, the Convention on Access to Information, Public Participation in Decision-making and Access to Justice in Environmental Matters and the Kyoto Protocol. It will then analyse the new type of NCPs, established in the Paris Agreement and the Rotterdam Convention on the Prior Informed Consent Procedure for Certain Hazardous Chemicals and Pesticides in International Trade and the Convention on the Protection and Use of Transboundary Watercourses and International Lakes. This chapter will analyse whether compliance is better ensured by more facilitative rather than coercive methods, together with NCPs’ legitimacy, including with reference to the powers of Conferences or Meetings of the Parties which mostly decide on non-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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.012 |
| 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".