Fuzzy truth values in option prioritization for preference elicitation in the Graph Model
Bibliographic record
Abstract
A methodology is developed to use the fuzzy truth values of preference statements for feasible states in an option prioritization technique in order to rank states within the framework of the Graph Model for Conflict Resolution. Option prioritization ranks states based on the truth values of preference statements, which are compositions of the decision makers' courses of actions joined by logical connectives, ordered lexicographically. Fuzzy truth values are represented as truth degrees; so they include binary truth values, “true” and “false”, as well as other possible truth intensities that are reasonable according to the specific circumstances. Consequently, the assumption of fuzzy truth values of preference statements provides more realistic preference ordering of feasible states. The methodology is applied to the Elmira groundwater contamination dispute, which took place in Elmira, Ontario, Canada, for eliciting the preferences of decision makers, to demonstrate the applicability of this technique.
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How this classification was reachedexpand
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".