Bibliographic record
Abstract
The intricacies, politics, and prospects of international cooperation, particularly with China, to address climate change. No country in the world releases more greenhouse gases than China. And no country has a greater capacity—and ambition—to mitigate climate change. This deeply informed, urgently needed book examines the global cooperation that such a monumental effort demands and inspires, necessarily focusing on China's outsize role in the development and dissemination of clean energy technologies. Drawing on decades of work in clean energy and climate technology and policy, Joanna Lewis provides a clear and thorough account of the motivations, science, and politics behind international clean energy technology collaboration—and an in-depth look at why different clean energy partnerships result in different political and technological outcomes. The first comprehensive analysis of international clean energy partnerships with China, Cooperating for the Climate is based on hundreds of interviews with government officials, researchers, and private companies involved in these collaborative initiatives around the world. Its insights into energy innovation and international relations, as well as global environmental politics, will help international stakeholders navigate the complex political bureaucracy governing clean energy development in China and perhaps chart a productive pathway for moving the world toward a low-carbon future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".