Decision support system for selecting engineering consultants using qualifications-based selection (QBS) and fuzzy TOPSIS
Bibliographic record
Abstract
In qualifications-based selection (QBS), consultants are selected according to their competencies rather than price. However, clients are often apprehensive about the subjectivity associated with implementing QBS because non-price criteria are hard to measure. In addition, there is no complete set of all relevant consultant evaluation criteria established. There is also a lack of an automated decision support system for objectively assisting owners in selecting qualified consultants with improved consistency and transparency. In this paper, a comprehensive set of consultant evaluation criteria is identified. Evaluation rules are also established for measuring qualitative criteria, where those rules determine the linguistic performance ratings for the fuzzy TOPSIS model instead of decision-makers, which minimizes subjectivity and increases transparency. The decision support system presented in this paper is flexible, allowing the decision-maker to adjust criteria weights based on the project characteristics and to exclude any non-applicable evaluation rules that may not fit in some projects.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".