Bibliographic record
Abstract
This chapter concerns with discrete multiattribute decision-making problems, in a group environment. One possible approach for solving this class of problems is to consider some aggregation procedures as the exclusive arbitration scheme to arrive at a collective decision. The chapter presents three strategies, based on different aggregation procedures, which can be utilized for extending multiattribute decision methods, related to the analysis of (X, R) models, to group settings. Among the main differences between these strategies, the chapter highlights the following: (1) the time at which the aggregation of the opinions becomes realized; (2) the way the experts are considered in the decision process; and (3) the character of numerical values being aggregated, say fuzzy estimates, fuzzy preference relations, and fuzzy nondominance degrees. The chapter includes some examples to illustrate how these strategies are utilized to solve group decision problems by means of different multiattribute decision methods. Controlled Vocabulary Terms aggregation
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.153 | 0.017 |
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; both teacher heads agree on what is shown here.
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".