Co-creating a festival with and for rural commoning initiatives: a transdisciplinary place-based process
Bibliographic record
Abstract
Transdisciplinary collaboration is well-established in sustainability science scholarship. Stakeholders from the cultural and artistic sector can significantly enrich the co-creation process by providing diverse perspectives on caring for people and places in marginalized areas. A key challenge is engaging these stakeholders early in the co-design phase to foster a sense of co-ownership and shared responsibility. To date, artists and cultural experts are mostly involved as service-providers, with little opportunity to embed their visions and values in the project at stake. Drawing on a legacy of transdisciplinary and participatory action research, we present an early collaboration involving architects, designers, cultural and sports association members, educators, farmers, workshop facilitators, and artists. This collaboration focused on co-creating a rural festival to showcase and connect commoning initiatives, and to foster a sense of care for community and environment, in a moment of vulnerability and isolation due to the COVID-19 pandemic. Our case study illustrates how festivals can catalyze enthusiasm and energy, engaging the wider public in lived experiences of collective care for social-ecological systems, and providing a platform for marginalized voices, challenging dominant narratives about rural livelihoods and well-being. The research highlights the potential for creating actionable knowledge that is reflective of local contexts and needs. Results also demonstrate how arts- and place-based methods can provide boundary objects that blur disciplinary differences and facilitate dialogue on complex and sensitive sustainability-related issues. We discuss how these methods can be further incorporated into transdisciplinary research and reflect on the practical and ethical challenges posed by early co-design and festive events.
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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 | 0.044 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.028 | 0.027 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".