Fixing the Climate: Strategies for an Uncertain World
Bibliographic record
Abstract
Global climate diplomacy — from the Kyoto Protocol to the Paris Agreement — is not working. Despite decades of sustained negotiations by world leaders, the climate crisis continues to worsen. The solution is within our grasp — but we will not achieve it through top-down global treaties or grand bargains among nations. Charles Sabel and David Victor explain why the profound transformations needed for deep cuts in emissions must arise locally, with government and business working together to experiment with new technologies, quickly learn the best solutions, and spread that information globally. Sabel and Victor show how some of the most iconic successes in environmental policy were products of this experimentalist approach to problem solving, such as the Montreal Protocol on the ozone layer, the rise of electric vehicles, and Europe’s success in controlling water pollution. They argue that the Paris Agreement is at best an umbrella under which local experimentation can push the technological frontier and help societies around the world learn how to deploy the technologies and policies needed to tackle this daunting global problem. A visionary book that fundamentally reorients our thinking about the climate crisis, Fixing the Climate is a road map to institutional design that can finally lead to self-sustaining reductions in emissions that years of global diplomacy have failed to deliver.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".