Approches qualitative et quantitative en évaluation de programmes
Bibliographic record
Abstract
Dans le domaine de l'évaluation de programmes, il est courant d'opposer évaluation qualitative et quantitative. Cet article remet en question le postulat suivant lequel approche qualitative et approche quantitative relèveraient de paradigmes différents. Il nous apparaît qu'une telle position correspond peu aux pratiques réelles et est de fait néfaste au développement des méthodologies en matière d'évaluation de programmes. Un examen des travaux récents en évaluation de programmes nous a permis d'identifier quatre démarches différentes qui nous semble susceptibles de faire évoluer la pratique de l'évaluation, à partir des critiques que s'adressent réciproquement les tenants des approches quantitative ou qualitative. La première de ces stratégies est celle de l'utilisation exclusive d'une approche, qu'elle soit qualitative ou quantitative. La seconde se réfère à l'utilisation de l'une ou l'autre des approches, suivant le contexte. Les troisième et quatrième combinent les deux approches, sous forme de triangulation ou d'intégration par combinaison.
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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 | 0.256 | 0.308 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".