An Effective Compromising Ranking Technique for Decision Making
Bibliographic record
Abstract
The VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method, which is a multi-criteria decision-making method, is examined in this paper. The VIKOR method, like other MCDM techniques such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), is widely used to solve complex decision-making problems in various fields such as engineering, management, and finance. This paper provides an overview of the VIKOR method, including its application areas, advantages, and disadvantages. Besides, in this survey paper, the process steps of the VIKOR method are described, including determining the decision matrix, normalizing the matrix, determining the weights of the criteria, calculating the utility and regret values, calculating the VIKOR index, and finally ranking the alternatives. By providing an overview of the VIKOR method and its process steps, this paper aims to provide a better understanding of the method and its potential application in different decision-making contexts.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".