Bibliographic record
Abstract
The evaluation of clustering quality has proven to be a difficult task. While it is generally agreed that application specific human assessment can provide a reasonable gold standard for clustering evaluation, the use of human assessors is not practical in many real situations. As a result, machine computable internal clustering quality measures (CQMs) are often used in the evaluation process. However, CQMs have their own drawbacks. Despite their extensive use in clustering research and applications, many CQMs have been shown to lack generality. In this paper we present a new CQM with general applicability. The basis of our CQM is a pattern recognition view of clustering's purpose: the unsupervised prediction of behavior from populations. This purpose translates naturally into our new classifier based CQM which we refer to as in formativeness. We show that in formativeness can satisfy core CQM axioms defined in prior research. Additionally, we provide experimental support, showing that in formativeness can outperform many established CQMs by detecting a larger variety of meaningful structures across a range of synthetic datasets, while at the same time exhibiting good performance on each individual dataset. Our results indicate that in formativeness provides a highly general and effective CQM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".