Reflections on the Meaning of Success in Collaborative Approaches to Evaluation: Results of an Empirical Study
Bibliographic record
Abstract
Abstract: How do evaluators using collaborative approaches to evaluation (CAE) define success? This is the core question being asked in a further analysis of data from our previous work (Cousins, Whitmore, & Shulha, 2013; Shulha et al., 2016) that developed a set of evidence-based principles to guide collaborative evaluation practice. Probing data from 320 responses to our (2012) survey, we examined what respondents considered “highly successful” and “less successful than hoped” in their collaborative evaluation projects. The results revealed that evaluation use, relationships, and information needs are key factors. We propose a conceptual framework as an aid to thinking about success in CAE.
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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 | 0.214 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| 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".