Multilevel Analysis of Factors Associated with Left Behind Children in China
Bibliographic record
Abstract
With the rapid development of urbanization in China, a growing number of rural workers migrate to urban cities for employment opportunities with leaving their children at home. These children are called left behind children (LBC) in China and their population has dramatically increased during the last 20 years. So far, many studies have examined what factors were associated with this increasing LBC populations. However, they were rarely guided by a holistic perspective. The current study investigated 1,691 left behind children in 166 communities using data from the China Health and Nutrition Survey (CHNS) in 2011. Based on the human ecology theory, this study explored family and contextual (community) characteristics associated with the left behind children (LBC) in China. The main results for this subpopulation of families with children revealed stark contrasts with the literature for the general population of migrants. That is, for the families with children, (1) contrary to the literature, father’s education was negatively associated with the probability of LBC at the individual level, even after the income was controlled; (2) community average father’s education was also negatively associated with LBC; but (3) community average household income was not associated with LBC once the average father’s education was controlled. The policy implications of these results are briefly discussed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".