Bibliographic record
Abstract
Illustrations General Editors' Preface Introduction Elizabeth Foyster, University of Cambridge, UK and James Marten, University of Milwaukee, USA 1 Family Relationships Joanne Bailey, Oxford Brookes University, UK 2 Community Alysa Levene, Oxford Brookes University, UK 3 Economy Deborah Simonton, University of Southern Denmark, Denmark 4 Geography and the Environment Giorgio Riello, University of Warwick, UK 5 Education Valentina K. Tikoff, DePaul University in Chicago, USA 6 Life Cycle Mary Abbott, Anglia Ruskin University, UK 7 The State Steven King, University of Leicester, UK 8 Faith and Religion Allison P. Coudert, University of California Davis, USA 9 Health and Science Mary Lindemann, University of Miami, USA 10 World Contexts Adriana Silvia Benzaquen, Mount Saint Vincent University, Canada Notes Bibliography Contributors Index
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.090 | 0.022 |
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".