An investigation into the impact of science communication and cognitive strain on attitudes towards climate change
Bibliographic record
Abstract
One of the most dramatic examples of the negative consequence of poor scientific communication is the issue of climate change, contributing to widespread mistrust and misunderstanding of how scientists do their work (Somerville & Hassol, 2011). Several studies have attempted to determine why there is such a discrepancy between the science community and people’s opinion of climate change. One such study measured participants’ skepticism about climate change before and after reading two newspaper editorials making opposing claims about the reality and seriousness of climate change. Results show significantly more skepticism about climate change after reading the editorial contradicting climate science (Corner, Whitmarsh, & Xenias 2012). Though science communication is a factor in individuals’ opinion of climate change, another study from the University of Maine found participants subjected to cognitive strain report more conservative political and social attitudes than the control group (Eidelman, Crandall, Goodman, & Blanchar, 2012). In the present study, we have combined these methods into one investigation to analyze the interaction between cognitive strain, the manner in which science information is presented, and attitudes toward climate change. Data were collected using in-person interviews. Political ideology was measured using the New Ecological Paradigm Scale (NEP, “a measure of endorsement of a “pro-ecological world view” (New Ecological Paradigm Scale, 2012)) and the Social and Economic Conservatism Scale (SECs) (Everett, 2013). Participants were randomly assigned to read one of three editorials, conveying positive, negative, or neutral perspectives on climate change, and the Stroop Test was administered to induce cognitive load in the experimental group. Finally, the Climate Change Skepticism scale (CCSs) was used to determine a participant’s attitudes toward climate change. Data were analyzed using the statistical analysis package, SPSS, to compare climate change attitudes between groups. We expected mentally taxed participants and those given the negative editorial to demonstrate significantly more skeptical views of climate change compared to participants not subjected to cognitive strain and those receiving neutral or positive editorials. Results from the present study show no effect of science communication or cognitive strain on attitudes toward climate change.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".