Human Cluster Evaluation and Formal Quality Measures: A Comparative Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clustering quality evaluation is an essential component of cluster analysis.Given the plethora of clustering techniques and their possible parameter settings, data analysts require sound means of comparing alternate partitions of the same data.When proposing a novel technique, researchers commonly apply two means of clustering quality evaluation.First, they apply formal Clustering Quality Measures (CQMs) to compare the results of the novel technique with those of previous algorithms.Second, they visually present the resultant partitions of the novel method and invite readers to see for themselves that it uncovers the correct partition.These two approaches are viewed as disjoint and complementary.Our study compares formal CQMs with human evaluations using a diverse set of measures based on a novel theoretical taxonomy.We find that some highly natural CQMs are in sharp contrast with human evaluations while others correlate well.Through a comparison of clustering experts and novices, as well as a consistency analysis, we support the hypothesis that clustering evaluation skill is present in the general population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.002 | 0.020 |
| Open science | 0.001 | 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