Guiding principles for transdisciplinary sustainability research and practice
Bibliographic record
Abstract
Abstract Transdisciplinary sustainability scientists are called to conduct research with community actors to understand and improve relations between people and nature. Yet, research hierarchies and power relations continue to favour western academic researchers who remain the gatekeepers of knowledge production and validation. To counter this imbalance, in 2018 we structured a multi‐day workshop to co‐design a set of principles to guide our own transdisciplinary, international and intercultural community of practice for biocultural diversity and sustainability. This community includes community collaborators, partner organizations, and early career and established researchers from Argentina, Bolivia, Canada, Germany, Mexico and South Africa. In 2021, we undertook online critical reflection workshops to share our research experiences and deepen our intercultural understanding of the application of the principles. Through these exercises, we adopted seven principles for working together that include: honour self‐determination and nationhood; commit to reciprocal relationships; co‐create the research agenda; approach research in a good way: embed relational accountability; generate meaningful benefits for communities; build in equity, diversity and inclusion; and emphasize critical reflection and shared learning. We explain these principles and briefly highlight their application to our research practices. By sharing these principles and associated practices, we seek to facilitate debate and spur transformations in how we conduct international and intercultural sustainability research. Our efforts also illustrate a strategy for on‐going knowledge co‐production as we cultivate safe and ethical spaces for learning together. Lessons learned may be particularly useful to those who engage in intercultural, collaborative research to advance sustainability transformations. Read the free Plain Language Summary for this article on the Journal blog.
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 | 0.025 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".