Informing the development of decision support tools for risk management: the case of electrical and magnetic fields
Bibliographic record
Abstract
This paper describes tools developed through a community consultative process to help decision makers manage electrical and magnetic fields (EMF) health risk. The process involved in‐depth interviews with experts (N=12) and focus group discussions with seven different stakeholder groups. The results reveal commonly held intense public concerns about the long‐term health effects of EMF. These concerns were further reinforced by the lack of public trust in both government and industry with regards to EMF risk management. Overall, the participants wanted tools that can be used to manage EMF information, scientific uncertainty about EMF and the complex environment in which EMF issues are embedded. The findings contributed to a mapping out of response formats to address public concerns related to risk, hazard, trust, accountability and fairness across a range of stakeholder groups. These tools and their roles in the management of complex and variable risks, involving new circumstances (e.g. privatization) and information (e.g. new scientific studies) are presented. The importance of recognizing and working with uncertainty through adaptive management strategies, using qualitative approaches, is also discussed.
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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 | 0.099 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".