Introducing Evidence-Based Principles to Guide Collaborative Approaches to Evaluation
Bibliographic record
Abstract
This article introduces a set of evidence-based principles to guide evaluation practice in contexts where evaluation knowledge is collaboratively produced by evaluators and stakeholders. The data from this study evolved in four phases: two pilot phases exploring the desirability of developing a set of principles; an online questionnaire survey that drew on the expertise of 320 practicing evaluators to identify dimensions, factors or characteristics that enhance or impede success in collaborative approaches in evaluation (CAE); and finally a validation phase involving a subsample of 58 evaluators who participated in the main phase. The principles introduced here stem from the experiences of evaluators who have engaged in CAE in a wide variety of evaluation settings and contexts and the lessons they have learned. They are understood to be interconnected and loosely temporally ordered. We expect the principles to evolve over time, as evaluators learn more about collaborative approaches in context. With this in mind, we pose questions for consideration to stimulate further inquiry.
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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 | 0.575 | 0.565 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.029 | 0.011 |
| Science and technology studies | 0.012 | 0.045 |
| Scholarly communication | 0.032 | 0.026 |
| Open science | 0.016 | 0.025 |
| Research integrity | 0.018 | 0.035 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".