Testing a Structured Decision Approach: Value‐Focused Thinking for Deliberative Risk Communication
Bibliographic record
Abstract
Public participation is now part of many decision making processes for managing environmental and technical risks. This article describes a test of a strategy to improve the quality of public input by combining themes from risk communication with the prescriptive decision process of value-focused thinking. It was hypothesized that participating in a structured, value-focused risk communication approach would lead people to make more thoughtful, better informed, and hence higher quality decisions by helping them to consider and discuss a wider array of decision-relevant issues and address key value trade-offs. It is also anticipated that utilizing a value-focused decision structure would make participants feel more comfortable with their decisions; more satisfied that their selected alternative reflected their key concerns; and, in the end, more satisfied with their decisions. To test these hypotheses, six groups comprised of 7 to 10 people participated in conventional "alternative-focused" risk communication workshops and eight groups participated in similar "value-focused" workshops. All workshops dealt with the management of risks to riverine salmon habitat from hydroelectric electricity generation. The results provided support for the hypotheses: the value-focused decision structure led to more thoughtful and better informed risk management decisions.
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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 | 0.126 | 0.311 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".