Bibliographic record
Abstract
This is the first annual report on non-compliance procedures (NCPs) to be included in the Yearbook for quite a few years. Examples are found in numerous multilateral and regional environmental agreements. These include the Montreal Protocol on Substances That Deplete the Ozone Layer, the Kyoto Protocol to the United Nations Framework Convention on Climate Change, the Cartagena Protocol on Biosafety, and the Convention on International Trade in Endangered Species of Wild Fauna and Flora. Regional examples include the United Nations Economic Commission for Europe (UNECE) agreements including the Convention on Long-Range Transboundary Air Pollution. Since the role of NCPs is sometimes misunderstood, this report first provides a brief overview of them, with a specific example of application. Second, it considers the application of two UNECE NCPs in 2016: the Compliance Committee of the Convention on Access to Information, Public Participation in Decision-Making and Access to Justice in Environmental Matters (Aarhus Convention) and the Implementation Committee of the Convention on Environmental Impact Assessment in a Transboundary Context (Espoo Convention).
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 | 0.061 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.081 | 0.071 |
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".