No. 25: Food Insecurity in Informal Settlements in Lilongwe, Malawi
Bibliographic record
Abstract
Although there is widespread food availability in urban areas across the Global South, it is not correlated with universal access to adequate amounts of nutritious foods. This report is based on a household survey conducted in 2015 in six low-income informal areas in Malawi’s capital city, where three-quarters of the population live in informal settlements. Understanding the dimensions of household food insecurity in these neighbourhoods is critical to sustainable and inclusive growth in Lilongwe. The survey findings provide a complementary perspective to the 2008 AFSUN survey conducted in Blantyre, which suggested a level of food security in urban Malawi that was probably more typical of peri-urban areas where many people farm. Given that informal settlements house most of Malawi’s urban residents, the Lilongwe research presents a serious public policy challenge for the country’s leaders. Poverty is a profound problem in Malawi’s rapidly expanding cities. Of particular concern is the poor quality of diets among residents of informal settlements. Precarity of income, reflected in the survey findings of frequent purchasing of staple foods and the need for food sellers to extend credit, appears to be a key driver of food insecurity in these communities. Economically inclusive growth, with better prospects for stable employment and protection for informal-sector workers, appears to be the surest route to improved urban food security in Malawi.
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.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".