COVID-19 and the food system: unpacking lessons from food traders’ responses in Tanzania
Bibliographic record
Abstract
The adverse impacts of the COVID-19 pandemic on the food system have underscored the vulnerabilities inherent in its various components, particularly on food trade, which experienced disproportionately severe effects. This study examines the experiences of food traders and their responses during and after the pandemic. It draws on intensive field research conducted in food markets across Arusha, Dar es Salaam, and Mwanza, as well as a review of evolving academic literature on COVID-19 and food systems. The results indicate a range of experiences among food traders, highlighting both substantial negative impacts on their businesses and unexpected gains from the crisis. In response to these disruptive effects, food traders employed a variety of strategies, including altering their sources of produce and credit arrangements, relying on social networks, engaging in collective purchasing and transportation of products, and utilizing digital platforms for customer interaction, ordering, payments, and delivery. The findings emphasize the need for policies and initiatives that enhance collective action among food system stakeholders, improve communication and public awareness during crises, and establish mechanisms for financial support and other incentives. Importantly, flexible and adaptive government policies can better address evolving dynamics and ensure food system functionality and resilience.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Field study of food traders' responses to COVID-19 in Tanzania; domain social science.
It studies food traders' responses to COVID-19 in Tanzania, not research itself.
Empirical study of food traders under COVID-19 in Tanzania; food systems research, not research-on-research.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".