Household vulnerability to food price increases: the 2008 crisis in urban Southern Africa
Bibliographic record
Abstract
Abstract Volatile food prices represent a common hazard to the food security of poor urban households. In trying to understand the impact of this hazard, income poverty is widely accepted as the principal predictive variable. But could other variables be important in understanding household vulnerability to food price shocks? This analysis uses survey data collected from 11 cities in Southern Africa by the African Food Security Urban Network during the 2008 food price crisis. As expected, the data show that household income is a significant predictor of the negative impact of rising food prices on household food security. However, other variables are significant predictors of household vulnerability to food insecurity as a result of food price increases. The analysis demonstrated how these diverse variables facilitated our classification of different households according to food price shocks using a CHAID decision tree. Demonstrating that household income is not the only significant predictor of household vulnerability to food price volatility, these findings broaden our understanding of the complex factors that can predispose households to food insecurity in the context of rising food prices.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".