New Modes of Becoming in Transcultural Glocal Spaces: Second-Generation Youth in Calgary, Winnipeg, and Toronto
Bibliographic record
Abstract
Second generation youth are currently the focus of much research and policy attention with respect to their integration, which is not yet well understood. Based on graphic and narrative data recently collected in three cities, Calgary, Winnipeg, and Toronto, we analyse second generation youth's patterns in glocal spaces where transcultural modes of belonging are created and lived. Our analysis focuses on attachments to locality and a continuum of mobilities of mind, body, and boundaries. The findings are interpreted in terms of the complexities of their integration processes as well as their relevance to social policy development. Les jeunes de la deuxième génération issue de l'immigration sont présentement au centre d'une attention particulière de la recherche et de la politique par rapport à leur intégration qui n'est pas encore très bien comprise. À partir de données graphiques et narratives récemment recueillies dans trois villes, Toronto, Winnipeg et Calgary, nous analysons les modèles de tels jeunes dans des espaces glocaux ou des modes d'appartenance transculturelles sont créés et vécues. L'analyse met l'accent sur leurs attaches à la localité ainsi qu'un continuum de mobilités de la pensée, du corps et des frontières. Les résultats sont interprétés par rapport à la complexité de leurs processus d'intégration et à leur pertinence par rapport au développement de politique sociale.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 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".