Being me in Canada: Multidimensional identity and belonging of Russian-speaking immigrant youths
Bibliographic record
Abstract
Although recent attention has focused on the experiences of immigrants in Canada, few researchers have explored the experiences of invisible immigrants, and Russian-speaking immigrants in particular, whose invisible nature may impact their identity and sense of belonging following their arrival in Canada. Specifically, they may not necessarily integrate with white mainstream Canadians, but they may also not fit in with their visible minority immigrant peers. Moreover, Russian-speaking immigrants often take an indirect path to Canada which may, in turn, have unique contributions to their acculturation experiences which may fit outside of current bicultural models of acculturation. The current study’s focus on immigrant youths is due to the developmental importance of identity and belonging during this time period. Moreover, the role of context in identity and sense of belonging, including peers and politics, will be explored as both factors have been overlooked in past research. Using constructivist grounded theory methodology, semi-structured interviews were conducted with 24 decimal- and second-generation Russian-speaking immigrant youths (15 to 19 years of age). A substantive theory of the identity and belonging of these youths was developed. Results indicated that the processes youths engaged in were multidirectional, flexible, and dynamic. At the core of the framework were three processes: choosing identities, expressing identities, and fitting in. The results of identity and belonging were often multidimensional, with youths choosing from and expressing more than one identity, and experiencing a sense of belonging with one or more groups. In order to facilitate these processes, youths navigated their context, with a particular focus on family, peers, community, politics, and immigration experiences. The youths’ experiences in navigating the context were both positive and negative which had an impact on their consequent identity and belonging. This study was one of first to consider the multidimensional nature of identity and belonging among immigrant youths, accounting for factors such as invisibility, indirect migration, and religious/cultural minority identities. Moreover, this study explored overlooked contextual factors such as peer group experiences (positive and negative, in-school and out of school, in-group and out-group) and the political context (multiculturalism and political conflict). Implications for research and practice are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".