Bibliographic record
Abstract
In this paper, we propose a novel approach to validating clusterings. We treat a given clustering as a baseline and define a collection of perturbations of it that give possibly different assignment of points to clusters. If these are indexed by a hyperparameter, integrating with respect to a prior gives an averaged assignment matrix. This matrix can be visualized as a heat map, allowing clusterings and their stability properties to be readily seen. The difference between an averaged assignment matrix and the baseline gives a measure of the stability of the baseline. This approach motivates a general and computationally fast algorithm for evaluating the stability of distance-based and exponential-model type clusterings, including k-means. In addition, these criteria can be used to choose the optimal number of clusters. Our method compares favorably with data based perturbation procedures, such as subsampling, in some conditions such as small sample size. In addition, there is evidence that our method performs better relative to subsampling methods on some problems. 1
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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 | 0.000 | 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 | 0.001 | 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".