Transdisciplinary sustainability research in real-world labs: success factors and methods for change
Bibliographic record
Abstract
Abstract The transdisciplinary research mode has gained prominence in the research on and for sustainability transformations. Yet, solution-oriented research addressing complex sustainability problems has become complex itself, with new transdisciplinary research formats being developed and tested for this purpose. Application of new formats offers learning potentials from experience. To this end, we accompanied fourteen research projects conceptualized as real-world labs (RwLs) from 2015 to 2018. RwLs were part of a funding program on ‘Science for Sustainability’ in the German federal state of Baden-Württemberg. Here, we combine conceptual and empirical work to a structured collection of experiences and provide a comprehensive account of RwLs. First, we outline characteristics of RwLs as transformation oriented, transdisciplinary research approach, using experiments, enabling learning and having a long-term orientation. Second, we outline eleven success factors and concrete design notes we gained through a survey of the 14 RwLs: (1) find the right balance between scientific and societal aims, (2) address the practitioners needs and restrictions, (3) make use of the experimentation concept, (4) actively communicate, (5) develop a ‘collaboration culture’, (6) be attached to concrete sites, (7) create lasting impact and transferability, (8) plan for sufficient time and financial means, (9) adaptability, (10) research-based learning, and (11) recognize dependency on external actors. Characteristics and success factors are combined to illustrate practical challenges in RwLs. Third, we show which methods could be used to cope with challenges in RwLs. We conclude discussing the state of debate on RwLs and outline future avenues of research.
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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 | 0.115 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".