A refined method for theory-based evaluation of the societal impacts of research
Bibliographic record
Abstract
With high and increasing expectations for research to have social and environmental impact, there is a corresponding need for appropriate methods to demonstrate (for accountability) and analyze (for learning) whether and how research projects contribute to change processes. Evaluation is especially challenging for problem-oriented research that employs inter- and transdisciplinary approaches and intervenes in complex systems, where experimental and statistical approaches to causal inference are inappropriate. Instead, theory-based evaluation can be applied to identify and test causal processes. This paper presents a detailed explanation of the Outcome Evaluation approach applied in Belcher et al. (2019b). It draws on concepts and approaches used in theory-based program evaluation and the more limited experience of theory-based research evaluation, providing a brief overview of conceptual strengths and limitations of other methods. The paper offers step-by-step guidance on application of the Outcome Evaluation approach, detailing how to: document a theory of change; determine data needs and sources; collect data; manage and analyze data; and present findings. This approach provides a clear conceptual and analytical framework in addition to actor-specific and impact pathway analyses for more precision in the assessment of outcomes. Specifically, the Outcome Evaluation approach: •Conceptualizes research within a complex system and explicitly recognizes the role of other actors, context, and external processes;•Utilizes a detailed actor-centred theory of change (ToC) as the analytical framework; and•Explicitly tests a set of hypotheses about the relationship between the research process/outputs and outcomes.
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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 | 0.282 | 0.408 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".