Bibliographic record
Abstract
Editors-in-Chief Graduate: Nicole Haldoupis Undergraduate: Caitilin TerflothSenior Editor, Health Sciences: Mariam GoubranAssociate Editors: Ashley Binta, Jamie Jinsoo Kim, Kristen Schott, Bahar BahraniGraduate Advisor: Chelsea CunninghamSenior Editor, Humanities and Fine Arts: Garret PifkoAssociate Editors: Kayden Gabriel Siriany Linares, Irteqa Khan, Renata Kisin, Erin Grant, Madison Taylor, Kaylee GuistSenior Editor, Interdisciplinary: Aimee FerreAssociate Editors: Emily Barlow, Nykole King, Tushita Patel, Brian VinetSenior Editor, Natural Sciences: Dakoda HermanAssociate Editors, Natural Sciences: Lavie Hoang Nguyen, Jeremy Young, Christina TollettGraduate Advisor: Rachel ParkinsonSenior Editor, Social Sciences: Courtney BallantyneAssociate Editors: Mitchell Barry, Rainer Kocsis, Lindsay Wileniec, Emma Bugg, Eric Gilliland, So Ri Lee, Michelle McLean, Whitney LoerzelGraduate Advisor: Jennifer SedgewickLayout Editor: Stephanie FuchsAssociate Editors: Michelle McLean, Lindsday WileniecMarketing and Communications: Veronica Stewart, with help from Erin Holcomb, Nicole Haldoupis, and Liv MarkenWebsite: Yashwanthan Manivannan and Liv Marken
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".