Bibliographic record
Abstract
Steven G. Affeldt (Le Moyne College)Isabel Andrade (Yachay Wasi)Stephanie Brown (Williams College)Alice Crary (University of Oxford/The New School)Byron Davies (National Autonomous University of Mexico)Thomas Dumm (Amherst College)Richard Eldridge (Swarthmore College)Yves Erard (University of Lausanne)Eli Friedlander (Tel Aviv University)Alonso Gamarra (McGill University)Paul Grimstad (Columbia University)Arata Hamawaki (Auburn University)Louisa Kania (Williams College)Nelly Lin-Schweitzer (Williams College)Richard Moran (Harvard University)Sianne Ngai (Stanford University)Bernie Rhie (Williams College)Lawrence Rhu (University of South Carolina)Eric Ritter (Vanderbilt University)William Rothman (University of Miami)Naoko Saito (Kyoto University)Don Selby (College of Staten Island, The City University of New York)P. Adams Sitney (Princeton University)Abraham D. Stone (University of California, Santa Cruz)Nicholas F. Stang (University of Toronto)Lindsay Waters (Harvard University Press)Kay Young (University of California, Santa Barbara)
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.493 | 0.269 |
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".