Risk Governance Dilemmas and Democratization: Public Trust, Risk Perception and Public Participation in Risk Decision-Making
Bibliographic record
Abstract
Abstract Risk scholars and practitioners are grappling with how best to govern risk in the face of growing calls and rationales for democratization. The centrality of public trust to effective risk governance, the fragmentation of perceptions of risk and growing expectations for public involvement in risk decision-making, all characterize risk governance in the twenty-first century. This chapter frames challenges to reforming risk decision-making as risk governance dilemmas. Effective risk governance requires confronting differences in expert and public perceptions of risk successfully, engaging the public meaningfully and fostering public trust in decisions. All three objectives can challenge fundamental epistemological, cultural and ontological underpinnings of risk governance. Understanding the reasons why this is the case (and why not), carefully disentangling causes and effects, and providing case studies of real-world efforts to address the dilemmas, lays the groundwork for informed reform of risk governance arrangements. There are no simple answers to the questions raised by the above three dilemmas. There is much to be learned about the strengths—and limitations—of opening risk decision-making processes to public participation. In addition to presenting the risk governance dilemmas running through the volume, this chapter presents @Risk, the research project on which this edited volume is based and provides an overview of the volume’s chapters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 teacher head, 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".