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
The Self-Organising Hierarchical Variance Map (SOHVM), a novel unsupervised clustering technique is proposed. Based on both Kohonen and Hebbian principles of self-organisation, the algorithm works to dynamically conauct a topology preserved mapping of dominant prototype clusters from within an unknown data source. Each neuron in the network consists of a dual memory element that tracks information regarding a discovered prototype. In addition to position, Hebbian based Maximum Eigenfilters (HME) simultaneously estimate the maximal variance of local data. Competitive Hebbian Learning (CHL) is used to dynamically associate prototypes such that an accurate topology is maintained throughout the discovery process. Knowledge may then be progressively imparted to the network through appropriate neighbouring memory elements. Vigilance is assessed via interplay between local variances such that more informed decisions control and naturally limit network growth. The approach is closely related to Self-organizing Tree Maps (SOTM), Growing Neural Gas (GNG) and their variants.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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