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.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.178 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".