ASSESSMENT OF RURAL HOUSEHOLD POVERTY STATUS INANKOBERWOREDA
Bibliographic record
Abstract
This study assessed the vulnerability of rural households to poverty in Ankober Woreda, Ethiopia, using a cross-sectional survey conducted in 2024. A total of 100 households weres ampled through a two-stage stratified random sampling technique. The Cost of Basic Needs(CBN) app roach was employed to establish the poverty line, set at 1860 Birr per month per adult equivalent. The findings revealed that approximately 68% of the households were vulnerabletopoverty, with significant correlations identified between vulnerability and factors such as family size, education level, income, farm size, age, sex, and oxen ownership. The analysis indicated that larger family sizes and lower educational attainment increased thelike lihood of poverty, while higher incomes and farm sizes contributed to lower vulnerability. The study highlighted the pressing need for targeted interventions to alleviate poverty, particularly through enhancing educational opportunities and providing support for off-farmingincome-generating activities. Recommendations included implementing family planning programs, creating non-farm job opportunities, and establishing oxen health centers to improve agricultural productivity. This comprehensive approach aimed to reduce vulnerabilityandpromote sustainable poverty alleviation in the region.
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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 | 0.001 |
| 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 | 0.000 |
| 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".