Culture, Politics and Climate Change : How Information Shapes our Common Future
Bibliographic record
Abstract
Overview Introduction Deserai A. Crow & Maxwell T. Boykoff Part 1 Culture and Climate Change Communication 1. Beyond gloom and doom or hope and possibility: Making Room for Both Sacrifice and Reward in Visions of a Low-Carbon Future Cheryl Hall 2. Polar Bears, Inuit Names, and Climate Citizenship: Understanding Climate Change Visual Culture through Green Consumerism, Environmental Philanthropy, and Indigeneity Doreen E. Martinez Commentary Mike Hulme Part 2 Media as Actors and Contributors to the Climate Politics and Policy 3. #Climatenews: Summit Journalism and Digital Networks Matthew Tegelberg, Dmitry Yagodin, and Adrienne Russell 4. TV Weathercasters and Climate Education in the Shadow of Climate Change Conflict Vanessa Schweizer, Sara Cobb, William Schroeder, Grace Chau, and Edward Maibach 5. Re-examining the Media-Policy Link: Climate Change and Government Elites in Peru Bruno Takahashi and Mark S. Meisner Commentary Joe Smith Part 3 Climate Politics and Policy 6. Climate Science, Populism, and the Democracy of Rejection Mark B. Brown 7. Explaining Information Sources in Climate Policy Debates Dallas J. Elgin and Christopher M. Weible 8. Navigating Controversies in Search of Neutrality: Analyzing Efforts by Public Think Tanks to Inform Climate Change Policy Jason Delborne Commentary Matthew C. Nisbet Part 4 Emerging Research in Climate Politics and Policy 9. Governing Subjectivities in a Carbon Constrained World Matthew Paterson and Johannes Stripple 10. Making Climate Science Communication Evidence-based-All the Way Down Dan Kahan Commentary Alison Anderson
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 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".