Political orientation and climate concern shape visual attention to climate change
Bibliographic record
Abstract
Despite the scientific consensus, there is widespread public controversy about climate change. Previous explanations focused on interpretations hampered by political bias or insufficient knowledge of climate facts. We propose that public views of climate change may also be related to an attentional bias at a more basic level of cognitive processing. We hypothesized that selective visual attention toward or away from climate-related information would be associated with climate concern. To test prioritization of climate-related stimuli under conditions of limited attention, we asked participants to identify climate-related and neutral words within a rapid stream of stimuli. Undergraduate students attended to climate-related words more readily than neutral words. This attentional prioritization correlated with self-rated climate concern. We then examined this relationship in a more diverse community sample. Principal Component Analysis of survey data in the community sample revealed a component indexing a relationship between climate concern and political orientation. That component was correlated with the degree of selective inattention to climate-related words. Our findings suggest that climate-related communications may be most effective if tailored in a manner accounting for how attentional priorities differ between audiences – particularly those with different political orientations.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".