Bibliographic record
Abstract
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.
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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 | 0.163 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".