A disaggregation approach for indirect preference elicitation in Electre <scp>TRI‐nC</scp>: Application and validation
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
Abstract Multicriteria sorting methods are often used in decision aiding contexts where the objective is to assign alternatives to predefined ordered categories. The Electre Tri family of sorting methods is based on pairwise comparisons of the alternatives with some, possibly fictional, alternatives that are either upper or lower limits of the categories (Electre Tri‐B), or one or more typical reference alternatives, that is, representative categories profiles (Electre Tri‐C, Tri‐nC). In this paper, we are interested in the Electre Tri‐nC method and in indirect preference elicitation based on partial information provided by the Decision Maker. We therefore propose, apply and evaluate a preference disaggregation method for learning criteria weights and the credibility threshold used in Electre Tri‐nC. The proposed disaggregation method is validated in an experiment using a climate classification problem for light tourism where 62,482 touristic locations are sorted into four categories. A robustness analysis of the method's performance using 150 learning sets is conducted and the results are presented and discussed.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it