Migration aspirations, life stage, and early-life socialisation experiences: examining age-cohort variations among Chinese migrants
Bibliographic record
Abstract
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.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".