Factors Influencing Involvement in Nonfarm Income Generating Activities Among Local Farmers: The Case of Ughelli South Local Government Area of Delta State, Nigeria
Why this work is in the frame
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Bibliographic record
Abstract
<p>The study investigated the factors influencing the involvement of farm families in non farm income generating activities. Multi — stage and stratified simple random sampling procedures were used to compose the sample. The sample was made up of seventy two (72) heads of rural farm families. Data were collected by use of structured interview schedule and Focus Group Discussion. Data were analysed by simple percentage, Chi square test, multiple regression and correlation There was a significant relationship between number of non farm income generating activities and demographic characteristics (R<sup>2</sup> = 0.870). Farm size (t = -2.386; p = 0.020), level of education (t = -4.227; p = 0.00), and household size (t = 5.404; p = 0.00) were significant and constant predictors of number of non farm income generating activities engaged in by the farm families. A significant relationship was found between involvement in non farm income generating activities and soil degradation due to infertility (X<sup>2</sup> = 23.66, p &lt; 0.01) and oil spillage (X<sup>2</sup> = 26.01, p &lt; 0.01). The study established a linear relationship between number of income generating activities and demographic characteristics. Extension workers should take into cognisance all rural income generating activities engaged in by the farm families when embarking on services and programmes aimed at improving their welfare.</p>
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it