Vers une réconciliation des théories et de la pratique de l’évaluation, perspectives d’avenir
Bibliographic record
Abstract
Evaluation practitioners encounter various questions while conducting evaluation projects. First, how can the evaluator define the intervention which is to be evaluated? Second, how should the evaluator consider the change? Third, how can use of the evaluation be encouraged? All three preoccupations have found answers in the theoretical developments of program evaluation, whether through implementation evaluation, intervention analysis or participative approaches. However, prolific theoretical developments, while enriching the strategies available to us, may also lead paradoxically to confusion and to difficulties in the transposition of new knowledge into practice. First, we will illustrate the three main difficulties the evaluator is confronted with during practice. Then we will review the different answers offered by evaluation theory. Finally, we will analyze the potential contributions and difficulties which these developments bring to evaluation practice. In conclusion, we will discuss future avenues for facilitating the appropriation of evaluation theories into practice.
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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 | 0.337 | 0.314 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.007 | 0.121 |
| Scholarly communication | 0.037 | 0.053 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".