Street traders’ contribution to food security: lessons from fresh produce traders’ experiences in South Africa during Covid-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Street traders play a key role in the food system in South Africa and many other countries. Despite their importance, the operations of street traders are not well understood and often undermined by policy makers and planners. This article provides insights into the role of street traders who sell food, in particular fresh produce, and the nature of their operations. It shares experiences of street traders in South Africa since the beginning of the Covid-19 pandemic and derives lessons from this for their contribution to food and nutrition security. The article is based on in-depth research carried out with street traders and other food system actors that they are linked to in three provinces (Gauteng, KwaZulu Natal and Limpopo) of South Africa. It was found that the street traders were severely affected during the first hard lockdown and continued to suffer due to the drop in aggregate demand that has resulted from the reduced incomes of many of their clients. They have also not been able to access the government Covid-19 recovery funds. Despite these challenges, street traders have continued to perform an even more essential role in making fresh produce accessible. This is in contrast to supermarkets that have maintained higher prices and profit margins despite the state of disaster affecting people’s ability to buy. Street traders are deserving of greater recognition and support as they play a key role in achieving food security and addressing other socio-economic challenges. Improving the conditions for street traders requires securing more public space for food trading and recognising and building on the ways that street traders use space and organise their economic lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it