Printed in Canadafre Development Effectiveness Review of the Asian Development Bank
Bibliographic record
Abstract
CIDA’s Evaluation Division wishes to thank all who contributed to this review for their valued input, their constant and generous support, and their patience. Our thanks go to the team that carried out the review. It was led by Ted Freeman of Goss Gilroy Inc. and included team members from the firm, as well as from the Department for International Development (UK) and the Swedish Agency for Development Evaluation (SADEV). The Evaluation Division would also like to thank the management team of CIDA’s Multilateral Development Institutions Directorate (Multilateral and Global Programs Branch) at Headquarters in Gatineau for its valuable support. Our thanks also go to the representatives of the ADB for their helpfulness and their useful, practical advice to the evaluators. From CIDA’s Evaluation Division, we wish to thank Michel Pilote, Project Manager, for overseeing this review and Brendan Warren, Junior Evaluation Officer, for his assistance with the review. We also thank Michelle Guertin, Evaluation Manager, for guiding this review to
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 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".