Beyond the targets: assessing the political credibility of pledges for the Paris Agreement
Bibliographic record
Abstract
This report provides the results of an analysis of “intended nationally determined contributions”, or INDCs, that were submitted by more than 180 countries ahead of the Paris climate change summit in December 2015, focusing on the credibility, rather than the ambition, of pledges about future emissions. No G20 country is found to have ‘no credible basis’ for their INDC across the determinants explored in this analysis. However, there are significant differences in the level of and balance among the determinants of credibility for the individual countries. Notably, three broad groups of countries can be identified: ◾Countries with most of the determinants at a level ‘largely supportive’ to credibility; this includes the EU and its individual G20 members (France, Germany, Italy and the UK), as well as South Korea; ◾Countries with most of the determinants at least ‘moderately supportive’ to credibility, but displaying significant weakness in one of the determinants; this includes Australia, Brazil, Japan, Mexico, Russia, Turkey, South Africa and the US; ◾Countries that have scope to significantly increase their credibility across most determinants. These are Argentina, Canada, China, India, Indonesia and Saudi Arabia.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".