Russian-speaking immigrants’ adaptation in Canada
Bibliographic record
Abstract
Abstract This article examines acculturation among Russian speakers in Canada focusing on immigration goals achievement, integration, feeling at home in Canada, and self-identity vis-à-vis the participants’ socio-demographic characteristics and language use. The study draws on data from a survey which was completed by 100 native speakers of Russian. The survey included Likert-scale responses and short answers analyzed quantitatively using Pearson correlations and chi-squares. The results indicate that most participants feel well-adjusted in Canada, they view immigration as the right decision and believe they have reached their immigration goals. However, about half of the respondents report experiencing discrimination, and only 20 % consider Canada their true home. In their self-identity expressions, their country of origin is prioritized. Correlations have been observed between the adaptation parameters and self-identity on the one hand, and the length of stay in Canada, participants’ age and age upon immigration, gender, and language use, on the other hand. These findings are crucial for immigrant help centers, ESL teachers, local governments and immigrants themselves (facilitating comparisons with peers’ immigration experiences). The results are interpreted in the light of Acculturation and Linguistic Equilibrium theories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".