Diverse actor perspectives on African urban food systems: lessons from participatory food system modeling in Worcester, South Africa
Bibliographic record
Abstract
Successful management of complex food systems inherently requires societal engagement. A major barrier is the misalignment between high-level generalized scientific representations of the urban food system and the varying practical perspectives of the actors embedded within it. To bridge this gap, participatory approaches can help in collecting and structuring knowledge from food system actors in a way that is understood by people with a diversity of experiences. Here, we showcase an approach to collect and synthesize diverse actor perspectives on the functioning of the urban food system in Worcester, a secondary city in South Africa. Together with six different groups of actors (N = 18) we built conceptual models of the urban food system and synthesized them into a full conceptual urban food system model. Our results show large differences in actor perspectives of the food system, including several (informal) subsystems that are often ignored in formal scientific food system models. Differences between actors in representation and in deemed importance of food system components can inform joint learning about the urban food system and enhance collaboration in finding food system solutions.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".