Leading inter- and transdisciplinary research: Lessons from applying theories of change to a strategic research program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it