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Record W4392373681 · doi:10.54097/7ek9wn03

The Analysis of the Situation of Migrant Students in China

2024· article· en· W4392373681 on OpenAlexaff
Qianrui He, Sujie Li, Zhenge Wang, Kedi Wu

Bibliographic record

VenueJournal of Education Humanities and Social Sciences · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Ethnic Minorities and Relations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChinaGeographyPsychologyPolitical scienceDemographic economicsSociologyEconomicsArchaeology

Abstract

fetched live from OpenAlex

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.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.049
GPT teacher head0.394
Teacher spread0.345 · 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 designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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