Les conditions gagnantes et les défis pour une participation active des parties prenantes dans un contexte d’évaluation de programmes d’intervention psychosociale
Bibliographic record
Abstract
Abstract: In order to optimize the appropriateness, utilization, and appropriation of an evaluation process, which is often expensive in terms of time and money, we have recently encouraged the increasingly active involvement of various stakeholders (Cousins et Chouinard, 2012). Beyond this orientation, there are important issues concerning the selection and the degree of participation of these stakeholders, as well as obstacles and conditions facilitating their participation (Hurteau, Houle et Marchand, 2012). Firstly, this article is based on the evaluation of Relais-Pères, carried out as part of participatory action research over more than ten years in order to specify the conditions and obstacles encountered in a framework of services. Secondly, attention is given to issues linked to the specifics of an evaluative approach in the interest of appropriation by partners in this approach.
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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 | 0.302 | 0.401 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".