Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Road Map on Statistics for SDGs was prepared by a team composed of members of the Conference of European Statisticians’ Steering Group on Statistics for SDGs: Renata Bielak (co-chair, Poland), Sara Frankl (co-chair, Sweden), Cara Williams (Canada), Maciej Truszczynski (Denmark), Claire Plateau (France), Kerstin Wichmann (Germany), Marina Gandolfo (Italy), Nazira Kerimalieva (Kyrgyzstan), Jelena Markovic (Montenegro), Lieneke Hoeksma (Netherlands), Natalia Ignatova (Russian Federation), Benjamin Rothen (Switzerland), Övünç Uysal (Turkey), Joanne Evans (United Kingdom of Great Britain and Northern Ireland), Kali Kong (United States of America), Elena Vosmirko (Interstate Statistical Committee of the Commonwealth of Independent States), Fritz Gebhard (Eurostat), Miriam Blumers (Eurostat), Guillaume Cohen (OECD), Tiina Luige (UNECE), Stela Derivolcov (UNECE), and the following contributing experts: Vjollca Simoni (Albania), Anahit Safyan (Armenia), Alexandra Wegscheider- Pichler (Austria), Charlotte Juul Hansen (Denmark), Mary Smyth-McCarthy (Ireland), Amit Yagur-Kroll (Israel), Magdalena Ambroch and Olga Swierkot-Struzewska (Poland), Carolina Santos and Ana Simão (Portugal), Ana Carmen Saura Vinuesa (Spain), Lisa Lundström and Cathy Krüger (Sweden), Ann Corp (United Kingdom of Great Britain and Northern Ireland) and Julia Schmidt (PARIS 21).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.051 | 0.002 |
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 it