No. 22: The Return of Food: Poverty and Urban Food Security in Zimbabwe after the Crisis
Bibliographic record
Abstract
The nadir of Zimbabwe’s political and economic crisis in 2008 coincided with the implementation of a baseline household food security survey in Harare by AFSUN. This survey found that households in lowincome urban areas in Zimbabwe’s capital were far worse off in terms of all the food insecurity and poverty indicators than households in the other 10 Southern African cities surveyed by AFSUN. The central question addressed in this report is whether food security in Zimbabwe’s urban centres has improved. AFSUN conducted a follow-up survey in 2012 that allows for direct longitudinal comparisons of continuity and change. The status of household food security in low-income neighbourhoods in Harare was improved in 2012 relative to 2008, and yet persistently high rates of severe food insecurity demonstrate that the daily need to access adequate food continued to be a major challenge. The key lesson for policymakers is that even in the context of overall economic improvement, food insecurity remains endemic among the poorest segments of the urban population. Households are already accustomed to drawing on resources outside of the formal economy and improvements in employment income have not reversed that trend. These alternative livelihood strategies should therefore be considered as a normal part of urban life and supported with state resources that can improve access to food for the most marginalized groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".