Bibliographic record
Abstract
Background: Participatory and collaborative evaluation approaches, including Empowerment Evaluation (EE), are useful for evaluating programs involving youth. Empowerment evaluation involves stakeholders in the evaluation process through a set of structured steps. It is primarily concerned with empowering, illuminating, and building program beneficiaries’ self-determination. Given the emphasis that EE places on inclusivity of stakeholders, it appears to be a good fit for evaluating programs that involve youth. Purpose: To explore the extent to which evaluators use EE to evaluate programs involving youth as well as what factor(s) facilitate and hinder their use of EE in these programs. Setting: The study involved evaluators associated with the Collaborative, Participatory and Empowerment Evaluation and Youth-Focused Evaluation Targeted Interest Groups (TIGs) of the American Evaluation Association (AEA) who are involved in evaluating programs targeted at youth. Intervention: Not applicable. Research Design: We used a two-phase sequential mixed-methods research design. In Phase 1, we surveyed evaluators. In Phase 2, we interviewed a sample of evaluators from Phase 1. Findings: In Phase 1, 41 (53.9%) respondents indicated not using EE to evaluate programs involving youth, 30 (39.5%) had used EE and 5 (6.6%) were unsure. Of those who used EE, they used it to teach youth program stakeholders about evaluation (n=8, 24.2%), produce more authentic results by engaging youth as experts of their lived experience (n=7, 21.2%) or produce more useful results for stakeholders to use (n=6, 18.2%), as well as other less popular reasons. In Phase 2, 12 interviewees raised five factors that facilitate or hinder the use of EE to evaluate programs involving youth including, evaluator perceptions, type of evaluation experience, evaluator knowledge and professional training, guidelines from organizations and funders, and stakeholders and time. Factors that some interviewees viewed as facilitators others viewed as hinderances.
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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 | 0.036 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".