Bibliographic record
Abstract
Front row, left to right: \nBernadette Gray-Little\nChancellor, University of Kansas\n\nBrian E. C. Schottlaender\nThe Audrey Geisel University Librarian, University of California, San Diego Libraries \n\nSkip Prichard\nPresident and CEO, OCLC\n\nSandra Yee\nDean of University Libraries & School of Library and Information Science, Wayne State University\n\nLarry P. Alford\nChief Librarian, University of Toronto Libraries\n\nCindy Hilsheimer \nManaging Principal, BeecherHill\n\nBarbara Lison\nDirector, Bremen Public Library\n\nBack row, left to right: \nLoretta Parham\nCEO and Director, Woodruff Library, Atlanta University Center \n\nKathleen Imhoff\nLibrary Consultant\n\nAnthony Ferguson \nAcademic Library and Information Consultant\n\nDavid Roselle\nDirector, Winterthur Museum and Country Estate\n\nEdward W. Barry\nPresident Emeritus, Oxford University Press \n\nJohn R. Patrick \nPresident, Attitude LLC\n\nBerndt Dugall\nDirektor a.D./Librarian, Universitätsbibliothek Johann Senckenberg, Universität Frankfurt \n\nJames G. Neal\nVice President for Information Services and University Librarian, Columbia University\n\nJennifer Younger\nExecutive Director, Catholic Research Resources Alliance
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".