Understanding and evaluating the impact of integrated problem-oriented research programmes: Concepts and considerations
Bibliographic record
Abstract
Abstract Researchers and research organizations are under increasing pressure to demonstrate that their work contributes to positive change and helps solve pressing societal challenges. There is a simultaneous trend towards more engaged transdisciplinary research that is complexity-aware and appreciates that change happens through systems transformation, not only through technological innovation. Appropriate evaluation approaches are needed to evidence research impact and generate learning for continual improvement. This is challenging in any research field, but especially for research that crosses disciplinary boundaries and intervenes in complex systems. Moreover, evaluation challenges at the project scale are compounded at the programme scale. The Forest, Trees and Agroforestry (FTA) research programme serves as an example of this evolution in research approach and the resulting evaluation challenges. FTA research is responding to the demand for greater impact with more engaged research following multiple pathways. However, research impact assessment in the CGIAR (Consultative Group on International Agricultural Research) was developed in a technology-centric context where counterfactual approaches of causal inference (experimental and quasi-experimental) predominate. Relying solely on such approaches is inappropriate for evaluating research contributions that target policy and institutional change and systems transformation. Instead, we propose a multifaceted, multi-scale, theory-based evaluation approach. This includes nested project- and programme-scale theories of change (ToCs); research quality assessment; theory-based outcome evaluations to empirically test ToCs and assess policy, institutional, and practice influence; experimental and quasi-experimental impact of FTA-informed ‘large n’ innovations; ex ante impact assessment to estimate potential impacts at scale; and logically and plausibly linking programme-level outcomes to secondary data on development and conservation status.
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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 | 0.258 | 0.326 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.035 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".