Vulnerability Profile of Rural Households in South West Nigeria
Bibliographic record
Abstract
This paper examined vulnerability to poverty of households among rural households in South West Nigeria usingprimary data from a two-wave panel survey (lean versus harvesting periods). Results showed that on the averagethere is a 0.56 probability of entering poverty a period ahead in the region and relatively high poverty rates wereassociated with much higher vulnerability while low poverty rates were associated with considerably lowvulnerability. Vulnerable households are mostly large sized with high number of dependants and characterized byunder aged or old, female headed, widowed household heads. They are mostly engaged in farming as their primaryoccupation, have no or low educational attainment and are landless. The findings underscore the centrality ofsocial protection policy mechanisms as potent poverty reduction tools and necessary policy interventions to reduceconsumption variability through reducing exposure to risk or improving the ex post coping mechanisms of thevulnerable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".