ARTICLE 7: LESSONS LEARNED AND THE CONTRIBUTIONS OF THE PARIS DECLARATION EVALUATION TO EVALUATION THEORY AND PRACTICE
Bibliographic record
Abstract
The final event of the Paris Declaration Evaluation was a lessons-learned workshop. This article first highlights the lessons about joint evaluations identified by participants in that workshop with the resulting report being a model of how to bring closure to a major evaluation. The article then presents 10 contributions of the Paris Declaration Evaluation to evaluation theory and practice. The article closes by recognizing that the Paris Declaration Evaluation received the 2012 American Evaluation Association (AEA) Outstanding Evaluation Award, which noted, “The success of the Evaluation required an unusually skilled, knowledgeable and committed evaluation team; a visionary, well-organized, and well-connected Secretariat to manage the logistics, international stakeholder meetings, and financial accounts; and a highly competent and respected Management Group to provide oversight and ensure the Evaluation’s independence and integrity. This was an extraordinary partnership where all involved understood their roles and carried out their responsibilities fully and effectively.”
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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 | 0.261 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.060 |
| Scholarly communication | 0.035 | 0.023 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".