Framing Scientific Analyses for Risk Management of Environmental Hazards by Communities: Case Studies with Seafood Safety Issues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Risk management provides a context for addressing environmental health hazards. Critical to this approach is the identification of key opportunities for participation. We applied a framework based on the National Research Council's (NRC) analytic-deliberative risk management dialogue model that illustrates two main iterative processes: informing and framing. The informing process involves conveying information from analyses of risk issues, often scientific, to all parties so they can participate in deliberation. In the framing process, ideas and concerns from stakeholder deliberations help determine what and how scientific analyses will be carried out. There are few activities through which affected parties can convey their ideas from deliberative processes for framing scientific analyses. The absence of participation results in one-way communication. The analytic-deliberative dialogue, as envisioned by the NRC and promoted by the National Institute of Environmental Health Sciences (NIEHS), underscores the importance of two-way communication. In this article we present case studies of three groups--an Asian and Pacific Islander community coalition and two Native American Tribes--active in framing scientific analyses of health risks related to contaminated seafood. Contacts with these organizations were established or enhanced through a regional NIEHS town meeting. The reasons for concern, participation, approaches, and funding sources were different for each group. Benefits from their activities include increased community involvement and ownership, better focusing of analytical processes, and improved accuracy and appropriateness of risk management. These examples present a spectrum of options for increasing community involvement in framing analyses and highlight the need for increased support of such activities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it