Towards an evidence base of theory-driven evaluations: Some questions for proponents of theory-driven evaluation
Bibliographic record
Abstract
This article discusses the further development of theory-driven evaluation approaches that are informed by contribution analysis. Using an illustrative example of an ongoing dance/physical activity programme for health promotion, a number of challenges are identified when applying a theory-driven evaluation approach. These challenges are reformulated as questions that need to be answered to make further progress with theory-driven evaluation including contribution analysis. Questions include: What is a ‘good enough’ programme theory? How does one arrive at expectations of programme impacts? How does the programme theory incorporate heterogeneous mechanisms that programme recipients might need? What does causality mean for complex interventions? What are structures that can facilitate learning from evaluations? How does the application of theory-driven evaluation approaches help generate an ‘ecology of evidence’? Discussion of these questions leads to a ‘roadmap’ for how contribution analysis might be further tested and refined.
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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 | 0.709 | 0.756 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.035 | 0.050 |
| Open science | 0.015 | 0.019 |
| Research integrity | 0.021 | 0.042 |
| Insufficient payload (model declined to judge) | 0.006 | 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".