Russian-speaking immigrants’ adaptation in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it