Trans-local reflexive learning for urban food system transformation: developing an approach while doing it
Bibliographic record
Abstract
In the last decade, on a global scale, many local initiatives have emerged aimed at multi-actor urban food system transformation. The challenges that actors in the initiatives face in their attempts to transform the local food system are ideally scaffolded by reflexive learning, for example by reflecting on own practices but also through in-depth knowledge exchange with actors from comparable initiatives. However, such knowledge exchange is hard to arrange due to time and availability of the actors, their geographical distance, and the need to tailor the learning method so that learning needs of the one are matched with lessons learned of the other and vice versa. This study aimed to work towards time and distance independent translocal reflexive learning on transforming urban food systems, through a reflexive action oriented research approach. Researcher took the role of interviewers as well as translators and facilitators of the learning. Data was gathered and learning was facilitated by means of dialogic timeline interviews and workshops with (at least) coordinating actors of 15 urban food system transformation initiatives across Europe. In three cases, these interviews and workshops were held with multiple stakeholders in those initiatives - and representatives of another similar initiative - so as to create an equal playing field for responsible and reflexive urban food system transformation. Combined deductive and inductive transcript and researcher note analysis yielded insights in the activity logic of these initiatives, amongst which various strategies to address commonly experienced transformation challenges, as well as insights into the value of the learning approach. We conclude the study with recommendations for improved facilitation of time and place independent reflexive translocal learning between actors who coordinate urban food system initiatives and their stakeholders, and further research directions.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".