Language attitudes and identity of Russian immigrants in Canada
Bibliographic record
Abstract
This presentation describes dynamics of language attitudes by first-generation Russian-speaking immigrants in Canada and their connections with identity. The state of bi/multilingualism in immigration involves a delicate balance between host and home countries’ languages (e.g., Tannenbaum & Peleg, 2020; Kühl, et al. 2020). Mastery of the host language is crucial for success in immigration (e.g., Hill et al., 2021), it also relates to identity (Cervatiuc, 2009). Maintenance of home languages and cultures is also often important for immigrant communities (e.g., Meddegama, 2020) and for ethnic identity (Tannenbaum & Peleg, 2020). However, the dynamics of language attitudes and identity change over the years of immigration remain underexplored.\nThe presentation focuses on examining the importance of learning the host country’s majority languages (English and French) vis-à-vis maintenance of the home language as seen by the participants upon immigration and after a few years in Canada. The goal of the study is examining language proficiency and attitudes change over years of immigration and their connection with identity.\nResearch questions are:\n--What are language attitudes of Russian-speaking immigrants in terms of the importance of learning the majority languages vis-à-vis maintaining Russian, and whether/how they change over time?\n--What is the connection of language attitudes with identity?\nThe study tool is an online survey of language dynamics in immigration. One hundred Russian-speaking immigrants from seven Canadian provinces took part in the study. The analysis involves quantitative comparisons of responses involving correlation and chi-square tests. The results indicate that over the time since immigration, the importance of the English language learning decreases, and the importance of Russian language maintenance increases for the participants, whereas the salience of acquiring French remains unchanged. Attitudes to the home language and culture correlate with identity (on the spectrum from Russian to Canadian). The results are interpreted through Linguistic Equilibrium Hypothesis of language dynamics in immigration.\nReferences\nCervatiuc, A. (2009) Identity, good language learning, and adult immigrants in Canada, Journal of Language, Identity & Education, 8(4), 254-271,\nHill, L. H.; Carr-Chellman, D., Rogers-Shaw, C. (2021). The challenges of immigration and implications for adult education practice. Adult Learning, 32(1), 3-4.\nKühl, K., Petersen, J. H., Hansen, G. F. (2020). The Corpus of American Danish: a language resource of spoken immigrant Danish in North and South America. Language Resources & Evaluation, 54(3), 831-849.\nMeddegama, I. V. (2020). Cultural values and practices: the pillars of heritage language maintenance endeavours within an immigrant multilingual Malayali community in the UK. International Journal of Bilingual Education & Bilingualism, 23(6), 643-656.\nTannenbaum, M. & Peleg, G. (2020). Language and identity among Iranian immigrants in Israel. Journal of Multilingual & Multicultural Development, 41(9), 764-778.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".