Cocina Colaboratorio: cooking transdisciplinary transformations of local food systems
Bibliographic record
Abstract
Transdisciplinary knowledge co-production has been deemed critical to support the transformative changes needed to navigate toward more just and sustainable futures. Novel collaborations between local stakeholders, artists, designers, and scientists have the potential to further advance such transformations. In this paper, we describe the work of the transdisciplinary project Cocina Colaboratorio. We describe how the project was born and established in three territories of Mexico. We explore how participatory artistic and design practices, centered around the kitchen, play out in creating and operationalizing arenas for exchange and experimentation. We depict the components of our theory of change, including the role of these arenas, individual and collective agency, and leverage points in the transformation of local food systems. We illustrate the challenges encountered and the opportunities to overcome them, namely finding common ground through diverse communication strategies, a collaboration protocol, monitoring, and iterative learning. We assess our outputs and products, the role of funding as an enabler and obstacle, and our strengths and weaknesses. Participatory artistic and design practices have a huge potential to nurture deeper and more meaningful transdisciplinary transformative research around the globe, and we aspire to make deep transformations in each of the three territories while contributing to global sustainability.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".