A Situated, Comparative Approach to the Everyday Transcultural Experiences of Migrant Youth
Bibliographic record
Abstract
Abstract Drawing on mixed-level data gathered through surveys and in-depth interviews with migrant youth from Melbourne (Australia), Toronto (Canada), and Birmingham (the UK), this paper offers deep socio-cultural insights into our understanding of everyday diversity and difference in reframed transformative, positive terms. Moving away from a dominant public discourse of disruptive, transgressive characterisations, this paper emphasises the relational possibilities and transcultural opportunities inherent in the emerging fluid identities and self-reflexivity of migrant youth. The paper focuses on migrant youth across diverse temporal characteristics along the settlement and integration journey. Synthesising from theories of transculturation, cultural adaptation, and social inclusion, the paper argues for new critical framings around belonging and relationality of the complex and multifaceted nature of transcultural being and living with everyday difference amongst migrant youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".