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
We investigate measures of the clusterability of data sets. Namely, ways to define how ‘strong ’ or ‘conclusive ’ is the clustering structure of a given data set. We address this issue with generality, aiming for conclusions that apply regardless of any particular clustering algorithm or any specific data generation model. We survey several notions of clusterability that have been discussed in the literature, as well as propose a new notion of data clusterability. Our comparison of these notions reveals that, although they all attempt to evaluate the same intuitive property, they are pairwise inconsistent. Our analysis discovers an interesting phenomenon; Although most of the common clustering tasks are NP-hard, finding a closeto-optimal clustering for well clusterable data sets is easy (computationally). We prove instances of this general claim with respect to the various clusterability notions that we discuss. Finally, we investigate how hard it is to determine the clusterability value of a given data set. In most cases, it turns out that this is an NP-hard problem. 1
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 | 0.000 | 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 it