Bibliographic record
Abstract
Assessments of the evolving climate change regime increasingly view it as a political and legal failure. The most common criticism of the recent UN conferences in Cancun and Copenhagen is that they did not result in binding legal agreements, and more generally that the climate regime does not contain enough hard-law elements. This is consistent with a general bias in favor of hard law exhibited by both scholars and practitioners, who tend to view softer outcomes as inferior or merely as temporary stepping stones on the way to “real” law. I argue that this hard law bias is counterproductive in the case of climate change, which presents especially difficult cooperation problems. I outline the ways in which the flexibility associated with soft law helps address both the efficiency concerns (how to reduce emission at the lowest aggregate cost and in the face of uncertainty) and distributive concerns (how to equalize costs and increase participation) that have plagued cooperation in this area. I argue the soft law strategy employed at Copenhagen and Cancun produced a number of negotiation successes that bode well for the future, and show that many of the hard law elements of the 1997 Kyoto Protocol were in fact counterproductive.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".