Applying the CGIAR Quality of Research for Development Framework to Process and Performance Evaluations
Bibliographic record
Abstract
CGIAR Evaluation Guidelines 2022Purpose:To harmonize the overall approach for evaluating research and science quality in CGIAR and similar contexts; to facilitate a common understanding of the Quality of Science (QoS) evaluation criterion and the methods. Audiences:The primary audience is evaluators, evaluation managers and commissioners involved in evaluating Quality of Science in CGIAR.The secondary audience is those responsible for providing inputs into evaluations in the CGIAR system.Users in other agricultural research-for-development (AR4D) contexts may find this document useful too. Framework and Policy Reference:This guidance supports the CGIAR Evaluation Framework and the CGIAR Evaluation Policy (2022) and should be read in conjunction with other evaluation-related guidelines.The framework 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 | 0.377 | 0.430 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.037 | 0.016 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".