Notice bibliographique
Résumé
To provide guidance to evaluators and stakeholders, evaluation scholars (i.e., those conducting research on program evaluation) have conducted numerous studies on the feasibility and effectiveness of using participatory and collaborative evaluation approaches in various contexts. While some participatory and collaborative evaluation approaches may involve youth in the evaluation of programs and interventions, few evaluations in this area have been formally documented and/or widely published. As a result, there remains a dearth of empirical research on participatory and collaborative evaluations involving youth. One such collaborative evaluation approach, empowerment evaluation (EE), appears to be well suited for engaging youth in program evaluation, as participants are co-evaluators. Using qualitative, quantitative, and mixed methods, EE aims to teach program stakeholders, including beneficiaries, how to conduct their own evaluations. In this two-part mixed methods research project I sought to investigate and formally document: (a) the use of EE for programs targeting youth; and/or (b) the involvement of youth in EE of such programs. By investigating and documenting these areas, this study builds on the very limited body of empirical research on EE. As such, it provides important information to evaluators who are embarking on evaluations of programs targeting youth, so that they can make informed decisions about the use of EE and the involvement of youth in their evaluation activities. To address these goals, this study used a mixed methods case study approach and included two parts and multiple phases. Part 1 Phase 1 involved a survey of evaluators associated with particular Targeted Interest Groups (TIGs) of the American Evaluation Association (AEA) who are involved in evaluating programs that target youth. It determined the extent to which: (a) evaluators report using EE to evaluate youth programs; and (b) how evaluators report involving youth in EE of youth programs. Part 1 Phase 2 involved interviews with a select group of these evaluators and explored what factor(s) facilitate and hinder: (a) the use of EE to evaluate programs involving youth; and (b) the involvement of youth in EE of programs targeting youth. Part 2 then used observations from an EE with youth of their science, technology, engineering and math (STEM) focused educational outreach program to explore: (a) what an EE of a youth program might look like in practice; (b) how youth can be involved in an EE. Youth also took part in follow-up interviews to allow an examination of: (c) the strengths and limitations of using an EE to evaluate a program targeting youth; and (d) the strengths and limitations of involving youth in an EE of a program targeting youth. Overall, the findings show that the use of EE to evaluate programs involving youth may be limited, however, there are factors that can facilitate and hinder the use of EE and the involvement of youth in EE. The findings also demonstrate that an EE can be carried out in practice with youth acting as co-evaluators and that through EE youth may experience both positive and negative outcomes of using EE and of being involved in EE. In light of these findings, ways to improve the involvement of youth in the evaluation of programs that target youth using EE are discussed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,163 | 0,176 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,008 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,008 |
| Communication savante | 0,013 | 0,018 |
| Science ouverte | 0,003 | 0,016 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».