Advances in Research on Social Networking in Open and Distributed Learning
Bibliographic record
Abstract
Lorem ipsum dolor sit amet, consectetur adipiscing elit. In quis ipsum aliquet, interdum lorem nec, blandit massa. Aliquam fringilla elementum erat vel convallis. Donec sed erat sagittis, accumsan ligula vel, eleifend tellus. Nulla ultrices lacinia lectus vel vestibulum. Duis aliquam sed elit ac pharetra. Donec lobortis id quam id tristique. Donec neque mauris, viverra vel blandit quis, consequat eu lacus. Nulla facilisi. Sed quis molestie arcu, et bibendum erat. Donec massa lectus, vehicula a mi id, eleifend vestibulum neque. Donec ut risus non lacus vehicula mattis at vel purus. Etiam auctor suscipit semper. Suspendisse urna turpis, condimentum in lobortis in.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".