A Research Agenda for Affective Dimensions in Climate Change Risk Perception and Risk Communication
Bibliographic record
Abstract
There is a growing body of literature that examines the role of affect and emotions in climate change risk perception and risk communication. Conceptions of affect and emotions have differed according to theoretical perspectives and disciplinary orientations (e.g., sociology of risk, psychology of risk, climate science communication), but little has been done to map these differences. This perspective article provides an in-depth analysis of the study of affect and emotions in climate change risk research through a literature review of studies published in the last 20 years. In this perspective, we examined how affect is conceived, what emotions have been considered, and their role in climate change risk perception and risk communication. Early studies in climate risk perception and risk communication included vaguely defined emotions (e.g., negative and positive) in climate risk perception and risk communication studies, more recently turning attention to how different affective dimensions interact with other factors, such as personal experience, knowledge, culture and worldviews, gender, and social norms. Using this review as a mapping exercise of the research landscape on affect and emotions in climate risk perception and communication, we suggest that future research could benefit from more interdisciplinary work that explores the role of different affective responses and their intensities before, during, and after climate-related events.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".