Evaluating policy-relevant research: lessons from a series of theory-based outcomes assessments
Bibliographic record
Abstract
Abstract The increasing external demand from research funders and research managers to assess, evaluate and demonstrate the quality and the effectiveness of research is well known. Less discussed, but equally important, is the evolving interest and use of research evaluation to support learning and adaptive management within research programmes. This is especially true in a research-for-development context where research competes with other worthy alternatives for overseas development assistance funding and where highly complex social, economic and ecological environments add to evaluation challenges. Researchers and research managers need to know whether and how their interventions are working to be able to adapt and improve their programmes as well as to be able to satisfy their funders. This paper presents a theory-based research evaluation approach that was developed and tested on four policy-relevant research activities: a long-term forest management research programme in the Congo Basin; a large research programme on forests and climate change; a multi-country research project on sustainable wetlands management, and; a research project of the furniture value chain in one district in Indonesia. The first used Contribution Analysis and the others used purpose-built outcome evaluation approaches that combined concepts and methods from several approaches. Each research evaluation began with documentation of a theory of change (ToC) that identified key actors, processes and results. Data collected through document reviews, key informant interviews and focus group discussions were analysed to test the ToCs against evidence of outcomes in the form of discourse, policy formulation and practice change. The approach proved valuable as a learning tool for researchers and research managers and it has facilitated communication with funders about actual and reasonable research contributions to change. Evaluations that employed a participatory approach with project scientists and partners noticeably supported team learning about past work and about possible adaptations for the future. In all four cases, the retrospective ToC development proved challenging and resulted in overly-simplistic ToCs. Further work is needed to draw on social scientific theories of knowledge translation and policy processes to develop and further test more sophisticated theories of change. This theory-based approach to research evaluation provides a valuable means of assessing research effectiveness (summative value) and supports learning and adaptation (formative value) at the project or programme scale. The approach is well suited to the research-for-development projects represented by the case studies, but it should be applicable to any research that aspires to have a societal impact. This article is published as part of a collection on the future of research assessment.
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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 | 0.711 | 0.724 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".