Multicriteria Decision-Making Methodology for Systems Engineering
Why this work is in the frame
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Bibliographic record
Abstract
A multicriteria decision-making methodology is proposed for decision making in systems engineering. A process is proposed for generating weights for evaluation criteria needed to evaluate design alternatives. A decision-maker classification is proposed based on the roles they play during the decision process. In order to accomplish this, in the first step, stakeholders' categorization is made, and their corresponding weights are determined representing their stake in decision. In the next step, each stakeholders' preference over the criteria set is determined, which leads to the ordinal rankings of the criteria for each stakeholder. In the following step, the stakeholders' criteria ordinal rankings are transformed into cardinal weights using the different decreasing utility functions. Thus, obtained final criteria weights are used for evaluation of the alternative design solutions. Optimality check measures are devised to select the appropriate decreasing utility functions.
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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 | 0.036 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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