Bibliographic record
Abstract
The Guidelines on Statistical Business Registers were prepared by the Committee of Experts on Business and Trade Statistics with the support of, and in collaboration with, the Statistics Division of the Department of Economic and Social Affairs. The following experts contributed to the drafting of the additional country examples: Luisa Ryan (Australia), Francisco de Souza Marta (Brazil), Tammy Hoogsteen (Canada), Zhuo Wang (China), Sayda Morera (Colombia), Zrinka Pavlović (Croatia), Søren Schiønning Andersen, Jens Christian Ring and Steen Eiberg Jørgensen (Denmark), Neveen Osama and Mennat Allah Mohamed Mossad Abou Hasswa (Egypt), Pierrette Schuhl (France), Gogita Todradze (Georgia), Lien Suharni, Rr. Nefriana, Tri Listianingrum, Wiling Alih Maha Ratri and Irien Kamaratih Arsiani (Indonesia), Leesha Delatie- Budair (Jamaica), Set Fong Cheung Tung Shing (Mauritius), Gerardo Durand (Mexico), D. Oyunbileg (Mongolia), Rico Konen (Kingdom of the Netherlands), Anne Abelsæth (Norway), Cristina Neves (Portugal), Sagaren Pillay, Marietha Gouws (South Africa), Priyadarshana (Sri Lanka), David Ackermann, Livio Lugano and Fabio Tommasini (Switzerland), Atef Ouni (Tunisia), Andrew Allen (United Kingdom of Great Britain and Northern Ireland), William Davie (United States of America), Saleh Alkafri (State of Palestine), Nikko Angelo Antonio (Asian Development Bank), Rami Peltola and Carsten Boldsen (Economic Commission for Europe), Merja Riitta Rantala, Biliana Branska-Lateva and Carsten Olsson (Eurostat), Manpreet Singh (International Labour Organization), and Alicia Hierro (International Monetary Fund).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".