The self in the mirror: fostering researchers’ reflexivity in transdisciplinary and transformative studies at the science-policy interface
Bibliographic record
Abstract
Reflexivity is a key expectation that researchers in transdisciplinary and transformative research for sustainable development need to meet. Its aim is to enable researchers to deal with normativity, to contribute to identifying and balancing different actors’ interests in processes of knowledge production, and to strengthen a pluralistic view of implicit assumptions. When designing and realizing transdisciplinary and transformative studies, researchers face a central question: How can we develop reflexive practices and live up to the demands of such work? Considering the important role that reflexivity plays in transdisciplinary approaches, it is surprising that only few approaches have explored the specific characteristics of reflexive practices empirically and analyzed how these practices are cultivated when doing transdisciplinary and transformative research. In this article we address this research gap by presenting and discussing a case in which researchers attempted to professionalize their reflexive practices at the science-policy interface (SPI). As part of the national Monitoring of Education for Sustainable Development in Germany, we used the method of collaborative autoethnography to systematically reflect on our own thinking and actions as researchers at the SPI over a period of 11 months. Based on an analysis of 66 situations in which we took field notes, we synthesized core topics of reflection and challenges encountered throughout the process (roles, relationship patterns, and normativity) in six collaborative interpretation sessions and analyzed them to understand our own practices of engagement within the field. Grounded in this analysis of our own selves as researchers looking in the mirror, we develop hypotheses about how our specific methodological approach helped us on a practical level to foster different kinds of reflexivity. With this two-fold approach, we aim to contribute to a better understanding of possible topics, challenges, and pathways of (increased) reflexivity among researchers working at the SPI.
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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 | 0.110 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.084 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".