Fuzzy Clustering and Mapping of Ordinal Values to Numerical
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Bibliographic record
Abstract
Classification of object is considered to be the first step in many computationally intelligent systems. Objects are categorized according to their features or characteristics. Objects in the same category can be clustered into groups according to the dissimilarity in terms of their features. These groups reveal some knowledge about the objects by their partitions. Features can be numerical, ordinal or nominal. There has not been a good way to measure the dissimilarity among ordinal values, which is required for clustering. We present a novel algorithm for developing a mapping of ordinal values to numerical values for which a measure of dissimilarity exists. The algorithm is made part of the fuzzy c-means clustering algorithm. The modified algorithm finds better partitioning into clusters as well as an ordinal-numerical mapping that reveals the hidden structural knowledge of the ordinal feature. Simulations show the method to be quite effective
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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