Meaningful transdisciplinary collaborations for sustainability: local, artistic, and scientific knowledge
Bibliographic record
Abstract
Meaningful transdisciplinary collaborations that weave diverse ways of knowing, doing, and feeling are increasingly recognized as central for enabling just and sustainable transformations. This Special Feature explores the unique contributions of art-science transdisciplinary collaborations in addressing complex social-ecological challenges. Drawing from a series of transdisciplinary projects, we examine how co-created processes between scientists, artists, and local knowledge holders foster new relational dynamics, challenge entrenched power structures, and expand the space for transformative action. The collaborations documented here highlight innovative approaches that emphasize local identities, shared values, emotional and aesthetic engagement, and long-term, caring relationships. We identify key mechanisms, such as participatory visioning, storytelling, material deliberation, and arts-based boundary objects, that facilitate individual and collective agency and deepen connection with place and community. Despite significant challenges, such as time constraints, power imbalances, and institutional inertia, these experiences illustrate the transformative potential of art-science collaborations when designed ethically, reflexively, and with epistemological pluralism. This editorial offers critical insights into the practices, conditions, and innovations that support meaningful art-science engagement, providing guidance for evaluating their impacts. As planetary crises intensify, such collaborations offer hopeful, grounded, and imaginative pathways toward more just and sustainable futures.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".