Bibliographic record
Abstract
With an eye toward developing more effective climate change education, social scientists have attempted to diagnose the reasons for lingering public skepticism of anthropogenic climate change. But rarely is the question addressed with the benefit of cross-cultural research. Geographer Simon Donner has demonstrated the utility of such an approach: drawing on a vast ethnographic and historical record, it is possible to surmise to what extent anthropogenic climate change skepticism stems from panhuman cognitive habits versus culturally and historically specific circumstances, with deep consequences back at home for climate education and citizen–climatologist dialogue. While building from this method, this article departs from Donner's reading of the ethnographic record as demonstrating a cross-culturally pervasive human intuition that the weather is beyond human influence, arguing instead for the role of culturally specific commitments such as the distinction between nature and society, “just world” belief, faith in progress, and system justification. Various climate change communication strategies based upon these alternate reasons for skepticism are suggested, and ultimately it is argued that the ideologically fraught nature of these beliefs takes the matter beyond the realm of “science education” into the arena of democratic dialogue.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 teacher head, 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".