Bibliographic record
Abstract
Abstract Climate change is frequently discussed by political figures and journalists that have divergent views on the validity of climate change; metaphor is often used to frame climate change and portray a particular stance on the issue. Religion metaphors used to invalidate the veracity of climate change have been documented in the United Kingdom and the United States ( Atanasova & Koteyko, 2017 ; Woods et al., 2012 ), however, our data indicates that specific Religion metaphors are also used to validate climate change. We address: (1) Which Religion metaphors are used to frame climate change as a valid issue, or as a fraudulent secular ‘religion’; (2) How these metaphors instantiate a specific ideological stance; (3) Patterns of metaphor use by climate change skeptics and climate change advocates. Our findings show that different Religion metaphors are used to frame climate change by conservatives and liberals in the United States and Canada. Through our analysis, we clarify how the Religion frame is used to make divergent arguments concerning climate change.
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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 | 0.002 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".