Bibliographic record
Abstract
Intuition, expressed as verbal arguments or axiom formulations, has often been used as a guiding principle for fuzzy number ranking (FNR). This chapter adopts the multi-attribute decision making (MADM) framework to analyze such intuition with three results. First, intuition in FNR should have involved multiple attributes, which are often implicated in the existing ranking methods. Then, we suggest three attributes (i.e., representative x-value, x-value range, overall membership ratio), which can be used to characterize the FNR intuition. Second, we decouple two issues in FNR: selection of attributes and aggregation of values, where aggregation is concerned with the trade-off among attributes to determine a single index for FNR. Then, the discount factors are proposed for the attributes of range and membership ratio to model the trade-off and formulate a ranking index. Third, the decoupling of attributes and aggregation reveals a fundamental tension between information content and the satisfaction of the FNR axioms. That is, if we can consider more information (in terms of attributes) as relevant to FNR, the ranking method will likely violate some FNR axioms. However, if we consider less information, the ranking method will be less sensitive to distinguish some fuzzy numbers for ranking. In the end, the proposed multi-attribute approach can provide a practical aspect to analyze and address the FNR problems.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".