How Do Evaluators Differentiate Successful From Less-Than-Successful Experiences With Collaborative Approaches to Evaluation?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: In this exploratory study, we wanted to know how evaluators differentiate collaborative approaches to evaluation (CAE) perceived to be successful from those perceived to be less-than-successful. METHOD: In an online questionnaire survey, we obtained 320 responses from evaluators who practice CAE (i.e., evaluations on which program stakeholders coproduce evaluation knowledge). Respondents identified two specific CAE projects from their own experience-one they believed to be "highly successful" and another they considered "far less successful than [they] had hoped."-and offered their comments and reflections about them. They rated the respective evaluations on 5-point opinion and frequency scales about (i) antecedent stakeholder perspectives, (ii) the purposes and justifications for collaborative inquiry, and (iii) the form such inquiry takes. FINDINGS: The results showed that successful evaluations, relative to their less-than-successful counterparts, tended to reflect higher levels of agreement among stakeholders about the focal program; higher intentionality estimates of evaluation justification and espoused purposes; and wider ranges and deeper levels of stakeholder participation. No differences were found for control of technical decision-making, and evaluators tended to lead evaluation decision making, regardless of success condition. DISCUSSION: The results are discussed in terms of implications for ongoing research on CAE.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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 it