Exploring the Driving Factors for Poverty among Rural Families
Bibliographic record
Abstract
Alleviating poverty is of great concern, which makes no poverty the first sustainable development goal of the United Nations. Understanding the factors responsible for household-level poverty is critical to achieving no poverty. Therefore, using the most vulnerable population, this study investigates rural families’ poverty status and its driving factors in Nigeria. The study used a structured questionnaire to collect cross-sectional data from 180 rural families. Data were analysed using the Foster-Greer-Thorbecke index and logistic regression. The results revealed that poverty is a serious issue among rural families, as 57.78% were poor, with a poverty depth and severity of 0.2493 and 0.0622, respectively. This shows that rural families are highly susceptible to poverty. The driving factors for the rural families’ poverty status were distance to market, credit, age, education, household size, farming experience, farm size, and marital status. The adopted coping mechanisms to poverty by rural families were increased cultivation, income diversification, reduced food consumption, selling properties, help from relatives and friends, child labour, collecting firewood, reduced spending, remittances, avoiding luxury items, and leasing land. These findings suggest policy interventions and strategies to achieve no poverty through the provision of affordable credit and transportation facilities for rural families.
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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) | 0.002 | 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".