ARTICLE 2: PREPARING, GOVERNING AND MANAGING THE PARIS DECLARATION EVALUATION
Bibliographic record
Abstract
Joint or multi-partner evaluations are evaluations of development cooperation policies, programs, and projects in which different donors, development agencies, and partner countries participate. The complexities of a large number of diverse stakeholders and multiple units of analysis in joint evaluations pose significant governance and management challenges to ensure the evaluation’s independence, credibility, quality, and utility. This article reports how governance and management were structured and operated to facilitate the evaluation of the Paris Declaration. A common evaluation framework was established to facilitate synthesis. The integrity of national evaluations had tobe ensured, including capacity-building and support as needed. National and international reference groups were established to ensure the engagement and buy-in of different stakeholder groups, including input and feedback to the core team that synthesized the results of the national evaluations. The article concludes with three important lessons about complex joint evaluations.
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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 | 0.135 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".