A WAR ON LANGUAGES: A LINGUISTIC SHIFT AMONG UKRAINIAN REFUGEES IN CANADA
Bibliographic record
Abstract
Language changes are often influenced by social changes (Sacco & Bossio, 2015; Saroj & Pal, 2020), as seen in the significant impact of the Ukrainian-Russian war on language use in Ukraine (Kulyk, 2015; Racek et al., 2024). While previous research has explored the shift from Ukrainian-Russian bilingualism to Ukrainian dominance within Ukraine (Shevchuk-Kliuzheva, 2020), very limited of research has emerged about this language shift among Ukrainians in other countries, particularly Ukrainian refugees. The 2022 Canada-Ukraine Authorization for Emergency Travel (CUAET) program facilitated the arrival of over 200,000 Ukrainian refugees in Canada (Paas-Lang, 2023). In a new environment, immigrants undergo an adjustment process known as “adaptation” (Berry, 1997). This process may affect their use of languages and attitudes towards them (Makarova et al., 2019). For Ukrainian refugees already navigating linguistic identity changes at home (Taranenko, 2023), this process becomes even more complicated, alongside typical immigrant difficulties (Berry & Hou, 2016). This study aims to outline the change in the use of languages by Ukrainian refugees in Canada, their attitudes to different languages, and what difficulties they are experiencing in their adaptation process. The methodology is a mixed method as I analyze the survey responses of 65 participants (descriptive and correlation statistics) as well as 23 interviews thematically analyzed (Braun & Clarke, 2006) with the NVivo package. The results demonstrate that the language shift that happened in 2022 was overwhelming and sudden, and affected almost all domains. The attitude change demonstrated a full switch from Russian language preference to a current severely negative attitude towards Russian language in both Canada and Ukraine. The adaptation process was significantly affected by the official languages’ proficiencies, such as having difficulties with finding a job or accessing services because of language barrier. However, the Ukrainian refugees demonstrated surprising resilience in trying to overcome difficulties by incorporating any means they had access to, such as translation applications. The findings of this study can inform future research on war-related language change among Ukrainians, as well as applied to create better resources to help all refugees to improve their adaptation and language-learning process in Canada.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".