More than scientists: How message and messenger attributes influence viewers’ climate change intentions
Bibliographic record
Abstract
Effectively motivating public action on climate change remains a central challenge for science communicators. This study investigated how message and messenger attributes shape viewers' motivation to act on climate change, and whether these effects vary as a function of political orientation. Using a policy-capturing design, 581 U.S. adults each viewed six randomly selected short videos from the More than Scientists website, in which climate scientists described the personal relevance of climate change. Linguistic features of the messages were analyzed using the Linguistic Inquiry and Word Count (LIWC) software, and messenger attributes (e.g., age, sex, attractiveness) were independently coded. Multilevel modeling revealed that messenger characteristics-particularly being older, male, attractive, and filmed in natural settings-were the strongest predictors of viewer motivation, explaining over 21% of within-person variance. By contrast, linguistic message attributes had weak predictive power overall, though messages with future-focused language and greater length were modestly more motivating. Political orientation moderated some message effects: affiliation-oriented language increased motivation for left-leaning viewers, while achievement-oriented language was more effective for right-leaning viewers. These findings underscore the importance of peripheral cues in climate communication and support targeted messaging strategies that align with audience values and identities.
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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 | 0.001 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".