What Can Be Learned from Experience with Scientific Advisory Committees in the Field of International Environmental Politics?
Bibliographic record
Abstract
Scientific advisory committees (SACs) are a critically important part of global environmental policy. This commentary reviews the role of SACs in six global and regional environmental regimes, defined here as the set of rules, norms, and procedures that are developed by states and international organizations out of their common concerns and used to organize common activities. First, SACs play a critical role in putting issues on the political agenda and the creation of an overarching regime. Second, the effectiveness of a given SAC and the associated regime is highly variable. Third, there is also considerable variation in the extent to which the regime is driven by an overarching scientific consensus, for example, high in the case of climate change, lower in the case of whaling. Fourth, the role of science in a given regime is also a function of whether the problem being addressed is relatively benign or more malign, that is to say, marked by deep political disagreements (i.e., climate change). Finally, the cases examined here suggest that the institutional design of the SAC matters and can influence the overall effectiveness of the SAC and by extension, the regime, but it is seldom decisive.
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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 | 0.042 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".