Support, opposition, emotion and contentious issue risk perception
Bibliographic record
Abstract
Purpose – Research on emotion in the context of risk perception has historically focused on negative emotions, and has emphasized the effect of these negative emotions on the perception of risk amongst those who oppose (rather than support) contentious issues. Drawing on theory, the purpose of this paper is to hypothesize that both positive and negative emotions are correlated with risk perceptions regarding contentious public issues and that this occurs amongst supporters and opponents alike. Design/methodology/approach – The paper explores the relationship between emotions and perceived risk through consideration of the highly contentious case of nuclear energy in Saskatchewan, Canada. The analysis uses data from a representative telephone survey of 1,355 residents. Findings – The results suggest that positive emotions, like negative emotions, are related to nuclear energy risk perceptions. Emotions are related to risk perception amongst both supporters and opponents. Research limitations/implications – The data set’s limited number of emotion measures and single public issue focus, combined with the survey’s cross-sectional design, make this research exploratory in nature. Future research should incorporate multiple positive emotions, explore opposition, and support across a range of contentious public issues, and consider experimental models to assess causal relationships. Practical implications – The paper offers insights into how public sector managers must be cognizant of the emotional underpinnings of risk perceptions amongst both supporters and opponents of contentious public issues. Originality/value – This paper builds on and expands previous work by considering both positive and negative emotions and both supporters and opponents of contentious issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 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".