Leading inter- and transdisciplinary research: Lessons from applying theories of change to a strategic research program
Bibliographic record
Abstract
Theory of Change (ToC) has been promoted as a useful tool in sustainability research for visioning, planning, communication, monitoring, evaluation and learning. It involves a mapping of steps towards a desired long-term goal supplemented with continuous reflection on how and why change is expected to happen in a particular context. However, there is limited reported experience with the development and application of ToCs in inter- and transdisciplinary research contexts. While some previous publications have focused on ex-post application, there has been little discussion about the process of developing and using ToCs in strategic planning and monitoring in large inter- and transdisciplinary research programs. This article reports challenges and lessons learned from the experience of developing and using ToCs in the inter- and transdisciplinary research program Wings (Water and sanitation innovations for non-grid solutions). Challenges include (1) managing time constraints, (2) balancing between concrete and abstract discussions, (3) ensuring diversity in group composition, (4) fluctuating between reservations and appreciation, and (5) fulfilling both service and science roles while leading the ToC process. The experience highlights the importance of alternating formal and informal interaction formats throughout the process, ensuring heterogenous group formation, involving early career scientists, being responsive to emergent needs and making the added value of developing and using ToCs explicit and tangible for all participants. Although these lessons are mainly derived from developing ToCs within the interdisciplinary program team, they can support other programs in both their inter- and transdisciplinary research endeavors.
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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 | 0.087 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.087 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.007 | 0.015 |
| 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; 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".