On the role of fuzzy decision support for risk communication among stakeholders
Bibliographic record
Abstract
Many of the environmental management issues which plague decision making processes can be poorly-defined scientifically, or misunderstood in real situations. Even as additional scientific studies clarify issues or impacts, (lack of) communication becomes the governing factor when external stakeholders are involved. In public participation processes, alternative dispute resolution techniques are being continually refined to enhance communication among stakeholders. The science of decision support is also evolving to allow direct participation of stakeholders in planning processes. This paper presents a framework for improving the public consultation process for the case of a popular recreational lake in central Alberta that receives discharge from a coal-fired power plant. Focus is given to the application of a fuzzy decision analysis technique to express subjectivities among stakeholders, and uncertainties in data representation. The fuzzy compromise approach is used to express subjective stakeholder evaluations for the purpose of providing feedback concerning the relative uncertainties in the performance for available alternatives, and for examining the implications of risk averse behaviour among stakeholders. The benefits of incorporating fuzzy sets to express subjectivity and model uncertainty is to clarify the relative performance of alternatives within a risk-based approach, and to identify data gaps which may impact the perception of alternatives or may be sensitive to diverse stakeholder perspectives.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".