Improved TOPSIS Model and its Application in the Evaluation of NCAA Basketball Coaches
Why this work is in the frame
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Bibliographic record
Abstract
Traditional TOPSIS model has some disadvantages, such as correlations between criteria, uncertainty in obtaining the weights only by objective methods or subjective methods and possibility of alternative closed to ideal point and nadir point concurrently, and many solutions have been proposed regarding these disadvantages. This paper presents a more systematic TOPSIS model, in which the correlations between criteria were overcome by a new method on evaluation index system based on R cluster analysis. It also proposes a combination weighting method which has considered subjective potency of human and the variance in the data. Besides, the possibility of alternative closed to ideal point and nadir point concurrently was avoided by vertical projection method and the measurement of similarity to solution was simplified by vertical projection distance. The feasibility and validity of this improved TOPSIS model were testified by the evaluation of NCAA basketball coaches after 1939.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it