Risk Perception and Its Impacts on Risk Governance
Bibliographic record
Abstract
Abstract Risk perception is an important component of risk governance, but it cannot and should not determine environmental policies. The reality is that people suffer and even die as a result of false information or perception biases. It is particularly important to be aware of intuitive heuristics and common biases in making inferences from information in a situation where personal or institutional decisions have far-reaching consequences. The gap between risk assessment and risk perception is an important aspect of environmental policy-making. Communicators, risk managers, as well as representatives of the media, stakeholders, and the affected public should be well informed about the results of risk perception and risk response studies. They should be aware of typical patterns of information processing and reasoning when they engage in designing communication programs and risk management measures. At the same time, the potential recipients of information should be cognizant of the major psychological and social mechanisms of perception as a means to avoid painful errors. To reach this goal of mutual enlightenment, it is crucial to understand the mechanisms and processes of how people perceive risks (with emphasis on environmental risks) and how they behave on the basis of their perceptions. Based on the insights from cognitive psychology, social psychology, micro-sociology, and behavioral studies, one can distill some basic lessons for risk governance that reflect universal characteristics of perception and that can be taken for granted in many different cultures and risk contexts. This task of mutual enlightenment on the basis of evidence-based research and investigations is constrained by complexity, uncertainty, and ambiguity in describing, assessing, and analyzing risks, in particular environmental risks. The idea that the “truth” needs to be framed in a way that the targeted audience understands the message is far too simple. In a stochastic and nonlinear understanding of (environmental) risk there are always several (scientifically) legitimate ways of representing scientific insights and causal inferences. Much knowledge in risk and disaster assessment is based on incomplete models, simplified simulations, and expert judgments with a high degree of uncertainty and ambiguity. The juxtaposition of scientific truth, on one hand, and erroneous risk perception, on the other hand, does not reflect the real situation and lends itself to a vision of expertocracy that is neither functionally correct nor democratically justified. The main challenge is to initiate a dialogue that incorporates the limits and uncertainties of scientific knowledge and also starts a learning process by which obvious misperceptions are corrected and the legitimate corridor of interpretation is jointly defined. In essence, expert opinion and lay perception need to be perceived as complementing rather than competing with each other. The very essence of responsible action is to make viable and morally justified decisions in the face of uncertainty based on a range of scientifically legitimate expert assessments. These assessments have to be embedded into the context of criteria for acceptable risks, trade-offs between risks to humans and ecosystems, equitable risk and benefit distribution, and precautionary measures. These criteria most precisely reflect the main concerns revealed by empirical studies on risk perception. Political decision-makers are therefore well advised to collect both ethically justifiable evaluation criteria and standards and the best available systematic knowledge that inform us about the performance of each risk source or disaster-reduction option according to criteria that have been identified and approved in a legitimate due process. Ultimately, decisions on acceptable risks have to be based on a subjective mix of factual evidence, attitudes toward uncertainties, and moral standards.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".