A Comprehensive Guide to the COPRAS method for Multi-Criteria Decision Making
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
MCDM has been utilized as a proficient decision-making technique for numerous decades. Complex Proportional Assessment (COPRAS) method, a prominent technique in multi-criteria decision-making (MCDM) which offers a systematic and effective framework for evaluating alternatives and making informed choices. The versatility of COPRAS is demonstrated via case studies across various domains, such as engineering, business, and environmental management, showcasing its adaptability and robustness in providing solutions to diverse decision-making scenarios. There is a lack of a comprehensive guide and a reviewing of application, strengths, and limitation for this method in the literature. Therefore, this study aims to offer an in-depth understanding of the COPRAS approach, including its applications, advantages, and disadvantages. Additionally, it provides detailed guidance on how to utilize the COPRAS methodology for decision-making and real-life problems.
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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 | 0.067 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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