Participatory Evaluation as Seen in a Vygotskian Framework
Bibliographic record
Abstract
Abstract: In participatory evaluations of K–12 programs, evaluators develop school faculty’s and administrators’ evaluation capacity by training them to conduct evaluation tasks and providing consultation while the tasks are conducted. A strong case can be made that the capacity building in these evaluations can be examined using a Vygotskian approach. We conducted participatory evaluations at 9 Hawaii public schools and collected data on the extent to which various factors affected participating school personnel’s learning about program evaluation. After the evaluations were completed, a trained interviewer conducted standardized interviews eliciting the participating school personnel’s opinions about the methods and effects of the capacity building. Two reviewers used codes representing Vygotskian concepts to categorize the interview results. We present the results of the coding and provide conclusions about the value of using a Vygotskian framework to examine capacity building in participatory evaluations.
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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 | 0.161 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.014 | 0.072 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".