Montreal Protocol at 30: The governance structure, the evolution, and the Kigali Amendment
Bibliographic record
Abstract
Scientific discoveries, national regulations, and international agreements impact our lives. If we bring all three together in a solid but flexible governance structure, then we are able to address those impacts and share more evenly their consequences across different nations. This is what the Montreal Protocol has done in its 30 years of life and will continue to do thanks to the recent Kigali Amendment. There are many lessons for diplomacy to be drawn from the recent negotiations, including the critical role of science. The most important lesson in reaching consensus is the injection of optimism, pride, ownership of the process, and building trust among all nations. The solid yet flexible foundation of the Montreal Protocol provided a firm grounding for the Kigali negotiations to experiment with the different negotiating techniques in a forum where countries, industry, non-governmental organizations, and scientists are brought together by the United Nations.
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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 | 0.032 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.024 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.024 | 0.024 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".