Urban Agriculture’s ‘Invisible’ Short Food Value Chain: How Small-scale Farming Contributes to Johannesburg Food Security
Bibliographic record
Abstract
Abstract Urbanisation into poverty in cities of the global South gives impetus to urban agriculture (UA) as a strategy to improve food security for low-income residents. This study disputes that UA is a trivial sector by arguing through the invisible short food value chain lens that it contributes to food security in local communities. The study adopted the extended case method that immersed researchers for more than a year to understand the practices of 11 farming entities and 20 of their customers in Johannesburg. Open-ended interview guides were administered to key informants from the city, provincial government, and non-governmental organisations. Findings show that UA increases food availability in local communities through the short food value chain. However, the local economy is undocumented and invisible to city stakeholders, negatively affecting their land use planning decisions for the sector. Though stakeholder consensus on UA is still lacking, the City of Johannesburg recognised UA’s potential and allocated both temporary and permanent land access arrangements for farming. Small-scale farmers lack the capacity to supply formal institutions, which can be overcome by intermediaries, such as civil society facilitation. The limitations of UA manifest in its inability to attract labour and keep records which inhibits its potential and support at the city level.
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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 | 0.003 |
| 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 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".