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
Network analysis relies largely on discovering similarity across communities, whilst a community can be strengthened with the help of content information.However, it is largely inhibited by noise that is present in most networks, especially in the link structure.This paper presents a basic approach to combine content with link information in graph-based structures to assist community discoveries.It also tries to reduce the impact of noises commonly found in social networking sites as well as Web-based information networks.We propose to calculate strength of a signal between nodes across the network by combining the link strength, which denotes the probability of that link lying inside a community, with similar content that can be estimated using cosine similarity, or the Jaccard coefficient.Furthermore, we discuss an edge-sampling process to retain locally-relevant edges for every node of the graph.The graph that results could be then clustered by using standard algorithms for community discoveries, such as Markov-clustering and METIS.We experimented on real-world datasets (Wikipedia, CiteSeer and Flickr) by changing sizes and parameters in order to understand the efficacy of our approach versus existing ones.We tried to find a beneficial method to combine approaches for a content and link analysis and a faster biased, graph-sampling approach.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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