MétaCan
Menu
Back to cohort
Record W2062025350 · doi:10.5539/ass.v6n12p176

Rural-Urban Migration and Its Consequences on Rural Children: An Empirical Study

2010· article· en· W2062025350 on OpenAlexvenueno aff
Syed Imran Ali Meerza

Bibliographic record

VenueAsian Social Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsRural areaAgricultureSocioeconomicsSample (material)Agricultural productivityEconomic shortageFood securityGeographyDemographic economicsEconomic growthEconomicsGovernment (linguistics)Medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.279
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueAsian Social ScienceSame topicRural development and sustainabilityFrench-language works237,207