UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE MEASURING SUSTAINABLE DEVELOPMENT Prepared in cooperation with the Organisation for Economic Co-operation and Development and the Statistical Office of the European Communities (Eurostat)
Bibliographic record
Abstract
The designations used and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Acknowledgements This publication is the result of the fruitful two years of productive cooperation of the members of the Joint UNECE/OECD/Eurostat Working Group on Statistics for Sustainable Development and its Steering Committee, chaired by Robert Smith from Statistics Canada. The work has benefited from the valuable contributions by the members of the Working Group who actively participated in the meetings. During the course of the work, many members of the Working Group and its Steering Committee have contributed papers as an input to the discussions. The list of authors who contributed papers is presented in the Bibliography of this publication. The Bureau of the Conference of European Statisticians has provided constructive guidance and assistance to the Working Group throughout the work. The UNECE provided secretariat support to the Working Group. The OECD and Eurostat also supported the work. Statistics Norway and the Norwegian Ministry of Finance have given financial support to research papers and to the Editor of the report.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".