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
Consider a random graph G of size N constructed according to a graphon w : [0, 1] 2 ↦ [0, 1] as follows. First embed N vertices V = { v 1 , v 2 ,…, v N } into the interval [0, 1], then for each i < j add an edge between v i , v j with probability w ( v i , v j ). Given only the adjacency matrix of the graph, we might expect to be able to approximately reconstruct the permutation σ for which v σ(1) < … < v σ( N ) if w satisfies the following linear embedding property introduced in [Janssen et al. Electron. J. Statist. 16 (2022) doi: 10.1214/21-EJS1940 ]: for each x , w ( x , y ) decreases as y moves away from x . For a large and non-parametric family of graphons, we show that (i) the popular spectral seriation algorithm [Atkins et al., SIAM J. Comput. 28 (1998) 297–310] provides a consistent estimator σ̂ of σ, and (ii) a small amount of post-processing results in an estimate σ̃ that converges to σ at a nearly-optimal rate, both as N → ∞.
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 | 0.001 |
| 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 | 0.000 |
| 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