The evaluation was conducted under the supervision of Jacques Laberge, Evaluation Manager and was assisted by Safeena Alarakhia, Performance Review Officer of the Evaluation Directorate. The Evaluation Team Leader Werner Meier of the Results-Based Managem
Bibliographic record
Abstract
background evaluation research. The Evaluation Directorate would like to thank the Evaluation Team for their hard work and diligence, their professional contribution to this important evaluation, and for their collective effort in addressing the challenges of a complex and arduous assignment. We would also like to acknowledge the assistance of the many individuals who made meaningful contributions to the overall evaluation process. This includes our colleagues within CIDA, the Department of Foreign Affairs and International Trade and Industry Canada as well as CIDA‘s Independent Evaluation Committee. Their readiness to facilitate the evaluation process, share their perspectives and provide valuable feedback on the draft reports was highly appreciated. Our thanks are also extended to the dedicated individuals in Canadian, multilateral and international implementing organisations who took the time from their busy schedules to meet with the team. Their contributions were essential for understanding the front-line perspective on the Canada Fund for Africa initiatives. The Evaluation Team benefited from a wide range of consultations, interviews, meetings, focus group sessions and online survey responses with African institution representatives and
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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 | 0.151 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".