Urban Informal Food Traders: A Rapid Qualitative Study of COVID-19 Lockdown Measures in South Africa
Bibliographic record
Abstract
Globally, the adoption of COVID-19 containment measures, such as lockdowns, have been used to curb the rapid spread of the pandemic. However, these action regulations have caused substantial challenges to livelihoods. We explored the perceptions and experiences of COVID-19 implications for urban informal food traders in South Africa during the initial lockdown period that lasted five weeks. A rapid qualitative study was conducted during October–November 2020. Twelve key informants (seven men and five women) categorized into informal traders and food system expert groups were interviewed. Data were analyzed thematically using MAXQDA software. Participants perceived informal trading as a main source of livelihood for many individuals. Negative lockdown impacts described included forced business closure, increased food costs and reduced demand. The consensus among participants was that the government’s lack of formal recognition for informal food traders pre-COVID-19 contributed to challenges they faced during the pandemic, as evidenced by their exclusion as “essential service providers’’ at the start of lockdown. Policies that fail to recognize and consider informal food traders during ‘normal’ times lead to widened social inequality gaps among already vulnerable groups during natural disasters and disease outbreaks. In the case of COVID-19 in South Africa, this caused severe hunger, food insecurity and income loss.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".