Rural-Urban Migration and Its Consequences on Rural Children: An Empirical Study
Bibliographic record
Abstract
Rural-urban adult migration, mainly adult male migration makes heavy demand on all family members, but especially on children who are left behind in rural area to shoulder the responsibility of agriculture production and food security. Labor shortage due to rural-urban adult migration may mean that children in rural area often have to face tighter time schedules and patterns of time use and human energy inputs required in agriculture production. The study revealed the impact of rural-urban migration on rural children. In the study, sample was restricted to households that own and/or operate agricultural land in rural area. A purposive sampling was adopted to select villages and covered 500 sample households. The study was based on link between rural-urban migration of adult persons and child labor in rural area. The empirical result showed that an additional rural migrant of a household increases the probability of having child worker in that household by approximately 51%. However, it was found that children of migrant households receive less preventive health care in their infancy. The study also showed that an additional adult worker of a household increases the probability of having child worker in that household by 29%. For this reason, this study supports the hypothesis that children are the last economic resource of a household.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".