Youths' Contribution to Household Welfare in Rural Areas
Bibliographic record
Abstract
Rural households are faced with low purchasing power, poor food consumption, and poor general well-being. Youths are economically active, which could contribute to reducing these problems and improving rural development. However, the extent to which youths contribute to improving rural household welfare has gained little attention. Thus, this study assessed youths’ contributions to household welfare and the factors influencing their contributions to household welfare. Data were collected from 180 youths using a structured questionnaire and then subjected to descriptive and multiple regression statistical analysis. The findings revealed that youths contribute significantly to rural households’ welfare. The monthly income, access to credit, association membership, gender, age, and access to remittances are responsible for the level of youths’ contributions to household welfare. The challenges that prevent youths’ contribution to household welfare in rural areas were poor government support, poor credit facilities, unemployment, lack of access to business information and lack of training opportunities and vocational programmes. Government support to rural youths through the provision of grants and loans as start-up capital is needed to empower youths to contribute to their household welfare. Also, youths should be encouraged to go for vocational training to acquire a skill that could be a vehicle for a source of income.
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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 | 0.001 | 0.002 |
| 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".