Bibliographic record
Abstract
Abstract The Montreal Protocol—the regime designed to protect the stratospheric ozone layer—has widely been hailed as the gold standard of global environmental governance and is one of few examples of international institutional cooperative arrangements successfully solving complex transnational problems. Although the stratospheric ozone layer still bears the impacts of ozone depleting substances (ODSs), the problem of ozone depletion is well on its way to being solved due to the protocol. This chapter examines how the protocol was designed and implemented in a way that has allowed it to successfully overcome a number of thorny challenges that most international environmental regimes must face: how to attract sufficient participation, how to promote compliance and manage non-compliance, how to strengthen commitments over time, how to neutralize or co-opt potential ‘veto players’, how to make the costs of implementation affordable, how to leverage public opinion in support of the regime’s goals, and, ultimately, how to promote the behavioural and policy changes needed to solve the problems and achieve the goals the regime was designed to solve. The chapter concludes that while some of the reasons for the Montreal Protocol’s success, such as fairly affordable, available substitutes for ODSs, are not easy to replicate, there are many other elements of this story that can be utilized when thinking about how to design solutions to other transnational environmental problems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".