Bibliographic record
Abstract
This chapter begins with an overview of the first-generation climate leadership of Al Gore, James Hansen, and Bill McKibben, all of whom can be broadly characterized as propounding a “tragic” and “catastrophizing” discourse. It then analyzes in-depth interviews with 14 second-generation climate leaders from the US, Canada, Mexico, the Philippines, Hungary, South Africa, Taiwan, and Australia, contextualized by recent academic research on this topic. This second generation of climate leaders offers a more “comic” discourse that acknowledges the tragic consequences of inaction but tends to avoid catastrophizing rhetoric, appealing to a sense of hope and possibility despite limited progress on this issue. The interviews lend credence to Simon Western’s conception of an emerging Eco-Leadership discourse (or paradigm), which is discussed in depth in Chapter 8. Narrative approaches to climate change leadership are discussed, along with a variety of pertinent research findings that illuminate the general contours of climate change leadership today.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.016 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.013 |
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; both teacher heads agree on what is shown here.
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".