‘Citizenship from below’: integration as assemblage for immigrant young people volunteers in Canada
Bibliographic record
Abstract
One prominent issue facing Canada as an immigrant society pertains to the social and cultural integration of immigrant young people, who are often criticised in academic and popular discourse as passive citizens. Adopting an explanatory sequential mixed method approach, this study examines the civic engagement and participation of immigrant young people, via volunteering, as a ‘citizenship from below’. The concept of assemblage as a theoretical framework allows for an understanding of social integration as practices of ‘citizenship from below’ and social formations that are affective, relational and configured. One layer of social integration views immigrant young people volunteering as an assemblage that provides them with agency evolving over time as they mature and move more towards a deeper connection to community to effect change as a form of lived citizenship. Second, developing a sense of belonging and identity with Canada illustrates an affective dimension or layer in the assemblage of social integration. The study also suggests that young people volunteering-as-assemblage is an integral part of social integration and citizenship that is endlessly being socially produced and assembled. The findings challenge the diffuse negative perceptions about immigrant young people as passive citizens and extant knowledge about the relationships among immigration, integration, citizenship and belonging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".