The Analysis of the Situation of Migrant Students in China
Bibliographic record
Abstract
Due to the household registration system (Hukou) acting as an institutional barrier, migrant children face a lot of challenges in China. They are prevented from gaining local education resources, such as library, computer lab and gym. This led to the formation of low-quality migrant schools to respond to strong schooling demand. So migrant children face more difficulties than urban students. They get poorer performances and are more vulnerable than urban children. Analyzing the situation of migrant children can help policy makers improve existing policies to enhance the quality of education for them. By summarizing several literatures, this paper found that except Hukou system, imperial examination in the history also caused the poor performance of migrant children. Besides, on society and family level, social work such as media publicity and in depth investigation into migrant family is recommended. In terms of government, more money from the government is needed to increase the quantity and quality in educational resources and institutions. Schools can organize activities to promote the integration of migrate children and apply advance teaching method to improve the academic achievement. Based on the current situation, several proposals are proposed to help migrant children in the final section.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".