Transcultural practices and inter-generational dynamics among migrant youth
Bibliographic record
Abstract
This paper explores intergenerational dynamics affecting second-generation migrant youth transcultural identities within a global context of increasing mobility, diversity and interconnectedness. Drawing on in-depth interviews of first and second-generation migrant youth across three research sites, this paper explores how migrant families in Melbourne (Australia), Toronto (Canada) and Birmingham (UK) maintain and transmit their heritage culture and associated values, skills and knowledge – a critical component of transcultural capital – to the next generation. The young adults’ narratives illustrate the socio-cultural processes that enable opportunities for inter-generational relating and cross-cultural belonging. The ensuing critical awareness, cross-cultural knowledge, and social engagement with one’s own culture(s) enhance intercultural openness, an important orientation in today’s hyper-connected and super-diverse world. Importantly, within this ethno-culturally pluralist framing, migrant youth act less as passive recipients of culture(s) and more as agentic drivers of multi-dimensional cultural adaptation. In mobilising selectively and agentically transcultural capital, migrant youth are then able to negotiate and critically engage with aspects of their heritage culture. This forms enabling strategies conducive to individually driven social empowerment and intercultural engagement.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".