Bibliographic record
Abstract
List of Abbreviations. Foreword. General Report Stefano Simontacchi . Australia Manuel Makas & Brian Lawrence. Belgium Laurens Narraina, Maarten Tas & Yves Voeten. Brazil Alvaro Taiar Junior & Ana Luiza. Bulgaria Kamena Valcheva & Ekaterina Dimitrova. Canada Christopher J. Potter, Chris Vangou & David Glicksman. Finland Samuli Makkonen & Visa Randell . France Bruno Lunghi, Philippe Emiel & Fabrice Rymarz. Germany Uwe Stoschek & Helge Dammann. Greece Vassilis Vizas. Hong Kong Kwok Kay So & Cathy Ng. Italy Fabrizio Acerbis & Daniele Di Michele. Japan Raymond A. Kahn & Hiroshi Takagi. Malaysia Jennifer Chang. Mexico David Cuellar (ITS Partner - Mexico Real Estate Tax Leader), Gabriel I. Aguilar Bustamante (Corporate Legal Services Partner) & Carlos Vela Trevino (Corporate Legal Services Manager). The Netherlands Jeroen Elink Schuurman & Serge de Lange. Singapore David Sandison & Teo Wee Hwee. South Korea Jin-Young Lee, Taejin Park & Yongjoon Yoon . Spain Antonio Sanchez & Jose L. Lucas. Turkey Ersun Bayraktaroolu & Baran Akan. United Kingdom Rosalind Rowe. United States Gary Cutson, John Gottfried, Alan Naragon, Kelly Nobis, Stephanie Tran, Jennifer Daubenspeck, Philip Sutton, Emily Pillars & Tom Kirtland. Appendix A. Appendix B
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.426 | 0.386 |
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".