The Effects of Climate Change Polarization on Endorsing Non-Normative Political Action
Bibliographic record
Abstract
Climate change has become one of the most polarizing issues in recent years and has become a topic of political division (Pew Research Centre, 2014). This research examines the potential relationships between perceived polarization and endorsing non-normative collective actions regarding climate change mitigation. Research suggests that when people perceive others as extreme, they are less optimistic about discussion, (Robinson et al., 1995), and more likely to endorse conflict escalating non-normative actions (e.g., blockades) over cooperative normative actions (e.g., petitions) because they believe their adversaries are incapable of objective reasoning (Kennedy & Pronin, 2008). We hypothesized that those who perceived greater polarization would be more likely to endorse non-normative collective political action. In this correlational study, 231 undergraduate participants completed an online self-report survey, which measured their degrees of personal polarization and perceived polarization of others, their position on climate change mitigation efforts, and their level of support for normative and non-normative political actions advocating for their position on climate change mitigation efforts. The results did not support our predictions; indeed, some effects were counter to hypotheses. Although those with more extreme personal attitudes supported both normative and non-normative action more than those with less extreme attitudes, those who perceived greater extremity in others’ positions endorsed non-normative actions less. Perceived extremity of others did not relate to endorsement of normative action. Faculty Mentor: Craig Blatz Department: Psychology NOTE: This work is available as an abstract only.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".