WHO/NMH/NPH/ALC/02.7 Distr.:general Original: English Global Survey on
Bibliographic record
Abstract
this report. IV The data collection would have been impossible without the support of all the National Focal Points in the participating countries. Our appreciation goes to: Australia: Anh Nguyen, Joseph Doyle, Austria: Phillip Kolloros, Margit Atzmller, Bosnia -- Herzegovina: Mirza Muminovic, Brazil: Caio Roberto Shwafaty de Siqueira, Vanessa Pinheiro, Bulgaria: Dragomir Draganovic, Canada: Reham Amin, Chile: Carolina Barnett, Arturo Borzutzky, China, Hong Kong SAR: Lee Wing Cheong, Leung Lok Sum, Joy Jenny Chu and Dr Christina Maw, Colombia: Marcela Fandio Crdenas, Croatia: Nikola Borojevic, Czech Republic: Eva Matejckova, Denmark: Matthias Zaccarin Lauritzen, Dominican Republic: David Soriano, El Salvador: Jorge Castellanos, Rodrigo Alfaro, Estonia: Kersti Kloch, Finland: Tom Sundell, France: Marc Sabourin, Germany: Michael Euler, Florian Striehl, Georgia: Levan Lebauri, Ghana: Philip Lamptey, Greece: Michael Samarinas, Guatemala: Myriam de Ybarra, Hungary: Eva Suranyi, Iceland: Bjrg Thorsteinsdottir, India: Amarinder Singh Bindra, Nadini Bura, Indonesia: Hartatiek Nila Kamila, Gercelina Silaen, Israel: Yuval Bloch, Noam Frey, Gil Shlamovitz, Italy: Tranquillo Antoniozzi, Soraya Zaid, Jamaica: Lincoln Cox, Japan: Yoshitaka Oyama, Gen Shinozaki; Kenya: Benedict Maungu, Kuwait: Al-Dousari Abdulrahman, Latvia: Aksels Ribenis, Lebanon: Hesham Khalfan, Ahmad Halwani, Lithuania: Tomas Vasylius, The former Yugoslav Republic of Macedonia: Vijay Rawal, Igor Ilievski, Malaysia: Irfan Mohamad, Malta: David Elluc, Gianfranco Spiteri, Mexico: Prof Luis Miguel Gutierrez Robledo, Nepal: Sanjeeb Sapkota, Netherlands: Jacco Veldhuyzen, Susanne van der Velde, New Zealand: Kirsten Gaerty, Nigeria: Jude Chimdi Ohanelle, Norway: Hilde Risstad, Palestinian Authority...
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.148 | 0.110 |
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".