L’étude d’évaluabilité : Une approche d’évaluation de programmes encore mal connue et peu utilisée
Bibliographic record
Abstract
Resume : L’etude d’evaluabilite (EE) a ete conceptualisee vers la fin des annees 1970 a la suite du constat de la mauvaise qualite d’implantation des programmes et de l’incapacite des evaluations a repondre aux besoins des parties prenantes. L’EE demeure, cependant, une approche d’evaluation de programmes encore peu utilisee malgre le fait qu’elle peut apporter une contribution substantielle a l’amelioration des programmes. En effet, ce type d’evaluation permet d’une part, de favoriser la clarification de la theorie du programme qui sous-tend la mise en œuvre des activites et d’autre part de faciliter l’elaboration du modele logique qui aide a l’implantation. Lorsqu’elle est conduite par un evaluateur ou une equipe d’evaluateurs qui possede les competences requises, elle permet egalement de disposer d’informations utiles a l’evaluation formative ou sommative qui sera entreprise. L’EEP fait partie des evaluations de type exploratoire que l’on peut realiser de quelques semaines a plusieurs mois en fonction de la complexite du programme et de son ampleur. Abstract: Evaluability assessment (EA) was conceptualized in the late 1970s following the finding of poor program implementation quality and inability of evaluations to meet stakeholders’ needs. EA is an approach that might allow improvement to both the program and the evaluation to be conducted later. In fact, EA may, on the one hand help clarify a program theory and on the other hand, facilitate the elaboration of the logic model, which are helpful for program implementation. When conducted by an evaluator or team of evaluators with the required skills, EA may also help to gather useful information in support of the program’s summative and formative evaluation. EA is considered an exploratory type of evaluations that can be conducted over a period of few weeks to several months, depending on the complexity of the program and its scope.
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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 | 0.088 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".