Global Cooperation and Understanding to Accelerate Climate Action
Bibliographic record
Abstract
International climate change negotiations continue to be slow and problematic, with long-standing differences between rich and developing countries difficult to overcome. Emission reduction pledges put forward at the United Nations Framework Convention on Climate Change (UNFCCC) meeting in Copenhagen in 2009 provided a strong foundation for progress, but a major gap remains between what is planned and emissions reductions consistent with a 2°C path. Despite some advance since COP 15 in Copenhagen, progress remains too slow if the world is to achieve a strong global deal by 2015; all countries need to do more. This chapter reviews recent progress in international climate change negotiations, explores prospects for global emissions to 2030 given current commitments, and discusses how to accelerate action, which will need to be on the scale of a new industrial revolution. There is already much action in the developed and developing world, but stronger policy is needed if we are to act on the scale required. This will involve both bottom-up (national policies) and top-down (collective international action) approaches, which support and encourage each other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".