Serrating the Russian Tongue: The Politics of Speaking Russian in the Ukrainian Community of Alberta.
Bibliographic record
Abstract
‘Should someone be considered Ukrainian if they speak Russian?’ This question has come up again and again throughout Ukraine’s 30 years of independence. Due to legacies of Russian colonialism and imperialism, Russian is the second most spoken language in Ukraine. Since 2014, at the start of the Russo-Ukrainian war, many Ukrainian citizens have started to shed their Russian language in favour of Ukrainian to show their support for Ukraine and to mark their identity as Ukrainian. However, after the full-scale invasion began in 2022, other research has shown that Ukrainians have become highly critical towards Russian-speaking Ukrainians who have not made the switch to Ukrainian. This thesis focuses on investigating these shifts in language ideology and practice among newly-arrived Ukrainians in Canada, to investigate how these newcomers were fitting in to a long-established and heavily Ukrainian-speaking immigrant community in Alberta. For this thesis, ten participants—all immigrants to Edmonton who arrived from Ukraine between 2019-2024—were interviewed about their ideologies and practices concerning the use of Russian and Ukrainian languages from the beginning of the Russ0-Ukrainian war in 2014, with a specific focus on the period since the full-scale invasion in 2022. Interview questions investigated if and how language practices among participants had shifted away from Russian toward Ukrainian, and why or why not this was the case. Supplemented by ethnographic observations and analysis of previous research on the topic, I demonstrate that while some participants have been switching to speaking mostly Ukrainian, this is not happening for all interviewees; rather, some are maintaining Russian, primarily in private spaces. This thesis provides a discussion of the nuances behind language choice among recent Ukrainian immigrants to the local community, and shows how dominant language ideologies and narratives are negotiated by members of this recent immigrant wave.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".