Understanding Food Security and Hunger in Xai-Xai, Mozambique
Bibliographic record
Abstract
Abstract The cyclical alternation of drought, cyclones and floods threaten food security for households in rapidly growing coastal cities such as Xai-Xai, Mozambique. Inhabitants of Xai-Xai are highly dependent on urban subsistence agriculture and informal markets in order to guarantee food for their households. Both of these food security strategies have been affected by natural disasters in recent years making it difficult for households to access food. Recent research discussed in this chapter demonstrates that urban households are deprived of basic needs and live under permanent stress manifested by their inability to provide a pot of xima meal on household’s tables. The area around Xai-Xai used to be the granary of the southern Mozambique, but it is no longer able to guarantee that role. A common response among Xai-Xai residents to questions about urban food security is that food security is a concept for experts who do not understand their lived experiences. To them, food security associated with the whole household having enough xima. This chapter examines the concept of food security from the perspective of what really matters to households in the context of extreme events. The chapter integrates the lead author’s reflections on her community’s experiences with hunger and food security during her childhood with recent research on food security in Mozambique. The significance of this method in this instance is, as stated above, to uncover food security experiences that may well escape rigorous quantitative methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".