MAKING IT REAL: WHAT RISK MANAGERS SHOULD KNOW ABOUT COMMUNITY ENGAGEMENT
Bibliographic record
Abstract
The "decide-announce-defend" approach to decision-making offers few meaningful opportunities for engagement in decision processes and communities and individuals frequently feel isolated from decisions. Correspondingly, many practitioners believe science is misunderstood by communities and that messages on risk are susceptible to distortion or misrepresentation. Many voices have called for more inclusive approaches to the analysis and management of risk. Here, we draw on theoretical and practical insights from the fields of risk communication, community engagement and contaminated land management, to explore some of the unique issues involved in communicating risk issues to lay audiences, and to identify principles for engaging communities in contaminated land risk management.
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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 | 0.055 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.045 |
| Scholarly communication | 0.026 | 0.065 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.022 | 0.031 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".