The spaces in between: an actor network analysis of alternative food systems in Latin America and the Caribbean
Bibliographic record
Abstract
For years, researchers and activists have claimed that alternative food systems are required to increase the resilience of low-income families in the Global South and overcome the negative effects of capitalist, agro-industry regimes. Urban and peri-urban agriculture and alternative forms of local food production and distribution in urban settings are often seen as promising strategies. Yet very little is still known about how local food initiatives emerge, are legitimized, and (sometimes) survive in contexts of informality. Here we use Michel Callon’s conceptual tools to reveal how alternative food networks are justified and operate in low-income settlements where informal construction and economic activities abound. We conducted 340 interviews and 312 questionnaires and followed 18 bottom-up initiatives related to food production, distribution, transformation and consumption in cities and peri-urban spaces in Colombia, Cuba, Ecuador, and Chile. Empirical results show how, seeking to stabilize food systems, local leaders create design outcomes and activities where social struggles, identities, and reified notions of time and space are key. From a practical perspective, these results show the inadequacy of regulations and policy to grasp the dynamic resistance occurring in the “in between” spaces of both identities and urban structure. Changes in urban policy and food programs are required to make sense of such complexity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".