Bibliographic record
Abstract
For over 50 years, evaluators have used theories of change to articulate the causal logic underpinning how an intervention is intended to bring about a desired change. From its origins in programme evaluation, the approach has been adopted more widely for purposes from program design to program management. As theories of change continue to be used for multiple purposes, it is an opportune moment for the evaluation community—where the approach originated—to provide their perspective on the strengths and limitations of the approach and its future directions. To provide these perspectives, we asked nearly 30 of the world’s leading evaluators and programme theorists to provide a short essay on the past, present, and future of theories of change. This book presents their insights organized into five main themes: the use of theories of change in broader public policy contexts; using theories of change to establish causality; developing theories of change reflective of multiple stakeholder perspectives; using theories of change to understand wider societal change processes; and applying theories of change approaches for multiple purposes. By sharing these diverse perspectives, the book aims to both provide evaluators and emerging programme theorists with critical perspectives to inform future practice. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".