Evolving foodscapes: Tracing trajectories of urban and peri-urban food sharing initiatives for just food transitions
Bibliographic record
Abstract
Urban and peri-urban (UPU) area food systems need reconfiguration to support just transitions towards sustainability. Collaborative acts around food – food sharing for brevity – have been mooted as a potentially productive arena for enacting such a transition, with research exploring the location, goals, and activities of individual food sharing initiatives (FSIs) internationally. Situated conceptually at the intersection of diverse economies approaches and critical mapping, with an overarching concern for achieving just transitions to sustainable food systems, this paper advances understanding of FSIs by adopting a novel longitudinal lens and focusing on the UPU scale. Implementing a co-designed and collaboratively translated system for identifying and categorizing FSIs that have a digital presence in two European cities: Milan and Barcelona, we contextualize and compare the results uncovered, contrasting these with findings from earlier research to establish evolutionary trajectories for urban FSI landscapes. The expanded mapping process offers significant empirical insights tracing the often invisible but dynamically evolving location, form, and function of UPU FSI landscapes. These methodological and empirical insights are interrogated to identify what contribution critical mapping of FSIs at the UPU scale makes to allied efforts for just and sustainable food systems. In conclusion, while the approach outlined has limitations in terms of resource intensity and explanatory power, we see the approach as one vital component in furthering comprehensive understanding of UPU food systems, providing opportunities to: document diverse food geographies; create new spatial imaginaries; support efforts for greater food democracy, and advocate for more equitable distribution of sustainable food sharing initiatives.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".