Clustering experts in linguistic environment: A hybrid method
Why this work is in the frame
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Bibliographic record
Abstract
Investigating clusters of experts is an interesting topic in the large-group decision-making (LGDM) problem, since being familiar with patterns (groups) of experts is beneficial to some other actions needed for decision-making (e.g., reconciliation of opinions derived from different expert groups). However, not too much attention has been paid to expert clustering in the LGDM problem under a linguistic environment. Besides, it seems that only the decision information is utilized to group experts while the auxiliary (outside) knowledge (e.g., expertise and occupation) about these experts has not been fully considered during the clustering process. To address this issue, this study proposes a hybrid method integrating outside knowledge about experts with practical preference information under the interval-valued linguistic environment to cluster experts. The method consists of four elements: pre-clustering of experts according to the given knowledge, the optimization model to transform the interval-valued 2-tuple linguistic (IV2TL) decision information, the data envelopment analysis-discriminant analysis (DEA-DA) model to deal with a two-cluster issue, and iterative clustering based on the DEA-DA model to cluster experts into multiple clusters. The feasibility and validity of the proposed method are illustrated with a real-world example. A comparison with the maximal tree clustering method in the linguistic environment is provided.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it