How to develop a realist programme theory using Margaret Archer’s structure–agency–culture framework: The case of adolescent accountability for sexual and reproductive health in urban resource-constrained settings
Bibliographic record
Abstract
Realist evaluation is in essence a theory-building and testing approach. We argue that in practice, the theory-building potential of realist evaluation, review and research is not fully exploited in the field of global health. Our assumption is that the Structure-Agency-Culture explanatory framework of critical realist Margaret Archer could stimulate realist evaluators to conceptualize and systematically explore how structural and cultural conditions interact with programmes that aim at introducing social change. We propose step-wise guidance towards integrating the Structure–Agency–Culture framework into the development of realist programme theories. We present a worked example from an urban adolescent health study in poor neighbourhoods of Kampala, Mumbai, New Delhi and Cotonou. The guidance aims to bring to the fore the role of agency and context through the analysis of the interactions between structure, culture, agency and mechanisms. This is helpful in realist research in general, and in evaluations of complex interventions oriented towards social change.
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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 | 0.064 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".