Community‐Based Research on Evaluation: Beyond Academic and Evaluator Perspectives
Bibliographic record
Abstract
ABSTRACT Despite research on evaluation (RoE) being valued for its ability to inform and improve practice, some have argued that due to the way RoE is currently conducted and shared, it may have limited practical value for evaluation professionals. In this study, we explore what “community‐based” RoE, or RoE conducted in community settings that captures voices beyond academic and evaluator perspectives, could look like. This foray into an inclusive perspective of RoE intends to fill gaps in published RoE literature and spark further interest in engaging community perspectives. We conducted three collaborative projects where evaluators worked in tandem with program community members and engaged with RoE. Our cross‐case analysis yielded three themes about RoE: the value of evaluation processes, the benefits of reflective practices, and the perceived value in learning from RoE by community organizations. We discuss the results in terms of process use, integrating evaluation into the organizational culture, and sustained interactivity with evaluation. We conclude with recommendations for RoE practices moving forward.
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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 | 0.272 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".