Neither right nor wrong? Ethics of collaboration in transformative research for sustainable futures
Bibliographic record
Abstract
Abstract Transformative research is a broad and loosely connected family of research disciplines and approaches, with the explicit normative ambition to fundamentally question the status quo, change the dominant structures, and support just sustainability transitions by working collaboratively with society. When engaging in such science-practice collaborations for transformative change in society, researchers experience ethical dilemmas. Amongst others, they must decide, what is worthwhile to be researched, whose reality is privileged, and whose knowledge is included. Yet, current institutionalised ethical standards, which largely follow the tradition of medical ethics, are insufficient to guide transformative researchers in navigating such dilemmas. In addressing this vacuum, the research community has started to develop peer guidance on what constitutes morally good behaviour. These formal and informal guidelines offer a repertoire to explain and justify positions and decisions. However, they are only helpful when they have become a part of researchers’ practical knowledge ‘in situ’. By focusing on situated research practices, the article addresses the need to develop an attitude of leaning into the uncertainty around what morally good behaviour constitutes. It also highlights the significance of combining this attitude with a critical reflexive practice both individually and collaboratively for answering questions around ‘how to’ as well as ‘what is the right thing to do’. Using a collaborative autoethnographic approach, the authors of this paper share their own ethical dilemmas in doing transformative research, discuss those, and relate them to a practical heuristic encompassing axiological, ontological, and epistemological considerations. The aim is to support building practical wisdom for the broader research community about how to navigate ethical questions arising in transformative research practice.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".