The Landscape of Risk Perception Research: A Scientometric Analysis
Bibliographic record
Abstract
Risk perception is important in organizational and societal governance contexts. This article presents a high-level analysis of risk perception research using Web of Science core collection databases, scientometrics methods and visualization tools. The focus is on trends in outputs, geographical and temporal trends, and patterns in the associated scientific categories. Thematic clusters and temporal dynamics of focus topics are identified using keyword analysis. A co-citation analysis is performed to identify the evolution of research fronts and key documents. The results indicate that research output is growing fast, with most contributions originating from western countries. The domain is highly interdisciplinary, rooted in psychology and social sciences, but branching into domains related to environmental sciences, medicine, and engineering. Significant research themes focus on perceptions related to health, with a focus on cancer, human immunodeficiency virus, and epidemiology, natural hazards and major disasters, traffic accidents, technological and industrial risks, and customer trust. Risk perception research originated from consumer choice decisions, with subsequent research fronts focusing on understanding the risk perception concept, and on developing taxonomies and measurement methods. Applied research fronts focus on environmental hazards, traffic accidents, breast cancer and, more recently, e-commerce transactions and flood risk. Based on the results, various avenues for future research are described.
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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 | 0.054 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.176 | 0.228 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".