MétaCan
Menu
Back to cohort
Record W4416268605 · doi:10.1080/1369183x.2025.2524592

Migration aspirations, life stage, and early-life socialisation experiences: examining age-cohort variations among Chinese migrants

2025· article· en· W4416268605 on OpenAlexaboutno aff
Wai Fong Chau

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationSocializationImmigrationLife course approachEmpirical researchMigration studies

Abstract

fetched live from OpenAlex

This paper examines motivational differences among different age-cohorts of wealthy Chinese migrants by incorporating the migration aspirations-capabilities framework with life-course analysis, combined with an analysis of migrants’ early-life socialisation experiences. Using empirical evidence generated from 56 semi-structured interviews with Chinese migrants living in Australia, Canada, the UK, and the US, the study discovers instrumental, future-oriented career aspirations among young post-1989 generation Chinese, and aspirations to enhance well-being among middle-aged pre-1989 generation Chinese. The findings indicate that differences in migration motivations across age cohorts are influenced by individuals’ life stages and early-life socialisation experiences. This paper contributes to Chinese migration studies by uncovering clear generational differences in the motivations and trajectories of Chinese emigration over the past two decades. Equally importantly, the paper advances the capabilities-aspirations analytical framework by incorporating an analysis of individuals’ life-course events and early-life socialisation experiences in their home countries, thereby providing a more thorough understanding of the complex formation of migration aspirations, and demonstrating its applicability in explaining variations in migration aspirations across different age cohorts.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.089
GPT teacher head0.387
Teacher spread0.298 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Ethnic and Migration StudiesSame topicMigration and Labor DynamicsFrench-language works237,207