Asian migration and education cultures in the Anglo-sphere
Bibliographic record
Abstract
Asian migration is transforming education cultures in the Anglo-sphere. This is epitomised in the mounting debates about ‘tiger mothers’ and ‘dragon children’, and competition and segregation in schools. Anxiety and aspiration within these spaces are increasingly ethnicised, with children of Asian migrants both admired and resented for their educational success. This paper presents some frameworks for understanding how Asian migration both shapes and impacts upon education outcomes, systems and cultures, focusing on Australia, the U.S., the U.K. and Canada. It challenges the cultural essentialism that prevails in academic and popular discussion of ‘Asian success’, arguing that educational behaviour cannot be reduced to ethnic categories, whether these are ethnic ‘learning styles’ (e.g. the ‘Chinese learner’) or ‘cultural’ family practices (e.g. ‘Confucian parenting’). In also presenting an overview of papers in this special issue, this introduction showcases the explanatory models offered by our authors, which locate Asian migrants within broader social, historical and geo-political contexts. This includes global markets and national policies around migration and education, classed trajectories and articulations, local formations of ‘ethnic capital’, and transnational assemblages that produce education and mobility as means for social advancement. These are the broader contexts within which education cultures are produced.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".