Motivated attention in the perception and action of climate change
Bibliographic record
Abstract
Despite the overwhelming scientific evidence, many people still remain skeptical about climate change and refuse to take actions to mitigate the adverse impacts of climate change. Here we propose a motivated attention framework to explain public skepticism and inaction. We propose that personal motivations (e.g., political orientation) shape attention to climate change information, which alters the perception of climate evidence and shifts subsequent actions to mitigate climate change. In Study 1 (N=700), participants viewed a graph representing the annual global temperature change from 1880 to 2014 and estimated the average temperature change. We found that participants gave a higher estimate when the data were framed as global temperature than when the temperature label was removed (in a neutral frame). Furthermore, political orientation predicted participants' estimation in that conservatives under-estimated the temperature change compared to liberals. In Study 2 (N=214), we eyetracked participants' gaze when they viewed the temperature graph, and found that liberals focused more on the increasing phase of the curve, which was associated with a higher estimation of the global temperature change. However, conservatives focused more on the flat phase of the curve, which was associated with a lower temperature estimation. In Study 3 (N=104), we found that the total amount of gaze fixations of liberal participants on the graph predicted their willingness to donate to environmental organizations and their donation amount. These results provide initial evidence for the motivated attention framework, highlighting an attentional divide between liberals and conservatives in the perception of climate data, which can further explain their polarizing beliefs about climate change, as well as the actions these individuals take to address climate change. The current findings have important implications for the visualization of climate data and communication of climate science to different socio-political groups. Meeting abstract presented at VSS 2018
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".