Why context matters: understanding transdisciplinary research through the lens of nine context factors
Bibliographic record
Abstract
Transdisciplinary research (TDR) integrates academic and non-academic expertise to co-produce actionable knowledge that contributes to societal impact in addressing sustainability challenges. While context is widely acknowledged as important, the role and definition of context factors shaping TDR remain underexplored. This study develops an integrative understanding of context by synthesising theoretical literature and analysing 17 semi-structured interviews from international TDR case studies. We identify nine key context factors across three categories: outer factors (outside projects), inner factors (within projects), and temporal/ spatial dimensions (project boundaries). These context factors influence collaborative research processes in different ways across projects, requiring ongoing reflexivity and adaptation. Positionality awareness and ethics are central in shaping power dynamics, stakeholder engagement, and knowledge-co-production, highlighting the need for context-sensitive approaches. To support this in a structured way, we present a framework linking context with research design, process, methods and outcomes. Additionally, we provide a set of reflective questions for researchers and practitioners to identify, assess, and respond to contextual influences that shape stainability transformations. By advancing a more systematic understanding of context, this study contributes to building reflexive and inclusive approaches to transdisciplinary collaboration.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".