MétaCan
Menu
Back to cohort
Record W4318822399 · doi:10.1177/23780231221149903

How Parental Internal Migration within China Affects the Aspirations of Left-Behind and Migrant Children: From Comparative and Multidimensional Perspectives

2023· article· en· W4318822399 on OpenAlexaff
Zhenxiang Chen

Bibliographic record

VenueSocius Sociological Research for a Dynamic World · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsChinaSocioeconomic statusLeft behindPerspective (graphical)Internal migrationMigrant workersDemographic economicsPersistence (discontinuity)Mechanism (biology)PsychologyDevelopmental psychologySociologyGeographyDemographyEconomic growthPopulationEconomics

Abstract

fetched live from OpenAlex

The author explores how parents’ internal migration within China affects their children’s socioeconomic aspirations and extends previous research by (1) comparing left-behind and migrant children, (2) considering multidimensional aspirations, and (3) testing mechanisms that explain the effects of parents’ migration on their children’s aspirational pathways. The first finding is that left-behind and migrant children have higher migratory aspirations than rural children. However, left-behind and migrant children do not differ from rural children in terms of occupational aspirations. The multidimensional perspective revealed that migrant children do not want mid-status or high-status occupations in smaller cities; rather, they prefer traditional rural-to-urban labor migration pathways, working in low-status occupations in big cities. Finally, the findings verified that most of the hypothesized mechanisms cannot explain the effects of parental migration. The persistence of the effects of parental migration on migrant children suggests that institutional mechanisms may exist to explain the effects.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.099
GPT teacher head0.415
Teacher spread0.315 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueSocius Sociological Research for a Dynamic WorldSame topicMigration and Labor DynamicsFrench-language works237,207