Towards an evidence base of theory-driven evaluations: Some questions for proponents of theory-driven 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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.104 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it