Bibliographic record
Abstract
The negotiations being conducted in the run-up to Paris provide not only an opportunity to finally agree to a substantial regime reform , but also a new occasion for China and the EU to fully (re-)establish their reputation in global climate politics and to durably contribute to a global regime whose viability will depend to a large extent on the commitment of the largest emitters. Central to the deal, as became apparent at Copenhagen, will be the contributions and positions of the two top emitters, China and the United States, as well as those of other emerging countries with rising emission profiles. The EU, whose emissions are in absolute and relative decline, arguably comes next in line, followed by developed countries such as Australia, Canada and Japan. At COP 15, both China and the EU seemed unprepared for their emerging new roles: China for responding to calls for leadership, and the EU for reacting to the fact that it was not asked to take on leadership. Both thus needed to develop strategies to better perform their new roles in the run-up to and at major global climate summits. Against this backdrop, this contribution asks what the EU and China can contribute to the Paris summit individually and, especially, collectively.
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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 | 0.004 | 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 | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".