Why do (some) people in informal settlements in Latin America grow food today and what is their struggle?
Bibliographic record
Abstract
For many years, researchers have debated whether urban and peri-urban Agriculture is a means to reach food security and alleviate poverty in the Global South. More recently, the COVID-19 pandemic, supply chain disruptions, and climate change impacts have fuelled optimism about the benefits of alternative food systems. Yet previous studies have shown that people who engage in alternative food systems often do it as part of a larger struggle and are not always seeking to solve food needs per se. Why are people in informal settlements in Latin America and the Caribbean engaging in alternative food systems and what is exactly their struggle? This study in low-income settlements in Colombia, Chile, Cuba, and Ecuador confirms that adult women and the elderly engage in alternative food systems for a variety reason that go beyond food. Common reasons include education, socialisation, environmental protection, leisure, crime reduction, maintaining cultural traditions, and dealing with psychological distress and isolation. The struggles within which these activities emerge take different forms and respond to specific local conditions. Involvement in food becomes a way of transforming space and expressing normative principles through collective action. But it is also a way of reifying values, (re)positioning individual identities and explore people’s experiences. From a theoretical viewpoint, these results show that to fully grasp the benefits of alternative food systems it is necessary to understand their spatial component and the specific forms of struggle that exist conditions of informality. Several tensions must be resolved in urban planning and food system projects.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".