Determinants of Off-Farm Labor Supply among Farming Households in Akwa Ibom State, Nigeria
Bibliographic record
Abstract
The study analyzes the various determinants of off-farm labor choice decision in Akwa Ibom State, Nigeria. Primary data collected from 120 respondents were employed in the study and analyzed using the logistic regression model. Result of the socio-economic characteristics of respondents revealed the prevalence of female farmers (63.3%), majority which were married (50%) with average household size of eight persons. Majority were educated with average experience of eighteen years. The prevailing off-farm work typology and pattern in the study area were self-employment (50%) and part time engagement (63.3%). Result of the logistic regression revealed that farm size, household size, total annual off-farm income and educational attainment of respondents were the major determinants of off-farm labor choice decision in the study area. This informed the need to pursue policies that would enhance educational attainment in the study area, enhance and stabilize income in the off-farm sector as well as educating and enlightening rural households, especially women on the benefit of off-farm work and the creation of enabling environment in rural areas through infrastructure provision with view to reducing migration to urban areas in search of off-farm work as the way out.
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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 | 0.001 | 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".