Bibliographic record
Abstract
Introduction, Gavin E. Oxburgh, Trond Myklebust, Allison Redlich and Dave Walsh 1. Indonesia, R. Dian Dia-an Muniroh and E. Aminudin Aziz 2. Iran, Hossein Raeesi, Mahnaz Parakand, Kamiar Alaei and Nakissa Jahanbani 3. Israel, Carmit Katz 4. Japan, Makiko Naka 5. South Korea, Misun Yi, Eunkyung Jo and Michael E. Lamb 6. Australia, Jane Tudor-Owen and Adrian J. Scott 7. New Zealand, Nina J. Westera, Rachel Zajac and Deirdre A. Brown 8. Belgium, Michel Carmans and Pierre Patiny 9. England and Wales, Genevieve Waterhouse, Anne Ridley, Rachel Wilcock and Ray Bull 10. Estonia, Kristjan Kask 11. France, Samuel Demarchi, Anais Taddei, Laurent Fanton, Herve Fabrizi and Stefania Tamasan 12. Germany, Renate Volbert and Bianca Baker 13. Italy, Angelo Zappala and Francesco Pompedda 14. The Netherlands, Imke Rispens and Jannie van der Sleen 15. Portugal, Carlos Eduardo Peixoto, Catarina Ribeiro, Raquel Veludo Fernandes and Telma Sousa Almeida 16. Scotland, Annabelle Nicol, David La Rooy and Stuart Houston 17. Scandinavia, Kristina Kepinska Jakobsen, Ivar A. Fahsing and Emma Roos af Hjelmsater 18. Slovenia, Tinkara Pavsic Mrevlje, Igor Areh and Sabina Zgaga 19. Switzerland, J. Courvoisier, A. Schaller and M. Cyr 20. Canada, Sonja P. Brubacher, Nicholas C. Bala, Kim Roberts and Heather Price 21. Chile, C. Navaro, D. Mettidofo and F. Garcia 22. Brazil, Lilian Milnitsky Stein, Gustavo Noronha de Avila and Luis Roberto Benia 23. USA, Kyndra C. Cleveland, Jodi A. Quas and Stephanie Denzel Conclusion, Gavin E. Oxburgh, Trond Myklebust, Allison Redlich and Dave Walsh.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.016 |
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".