Determinants of Rural Households’ Choice of Non-Farm Livelihood Patterns in Southeast Nigeria
Bibliographic record
Abstract
The study ascertained the determinants of the choice of non-farm livelihood patterns of rural households in Southeast Nigeria. The specific objectives were to: describe the socio-economic characteristics of rural households in the study area, identify the predominant non-farm livelihood patterns adopted by rural households and ascertain available livelihood resources and estimate the determinants of the choice of non-farm livelihood patterns among households in the study area. A five (5)–stage random sampling procedure was used in the selection of 360 samples for the study. A structured and validated interview schedule was used for data collection. Data were analyzed using mean, frequencies, percentages and ordered logit regression model. Results indicated that trading (mean = 3.98), commercial cars/motorcycle services (mean = 3.91), Bicycle repairing (mean = 3.71), tailoring and weaving (mean = 3.53), were the predominant non-farm livelihood patterns of the respondents. Furthermore, human capital (mean = 2.86) and social capital resources (mean = 3.13) were their available livelihood resources. Human capital resources (p = 0.001) and physical capital resources (p = 0.076) were the determinants of respondents’ choice of non-farm livelihood patterns. The study recommends that all stakeholders should intensify rural infrastructural development.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".