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 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 | 0.027 | 0.125 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.139 | 0.185 |
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".