Language Attitudes of the Mongolian Diaspora in Canada
Bibliographic record
Abstract
The Mongolian diaspora in Canada is relatively recent with the majority of migration occurring after the implementation of the 2001 Immigration and Refugee Protection Act. Previous research on minority languages and dominant languages among immigrant communities demonstrates complex relationships between language attitudes and identity formation (Canagarajah, 2013; Lustanski, 2009). However, while studies exist on various immigrant communities' language attitudes in Canada, the Mongolian diaspora's linguistic dynamics remain unexplored. This study investigates the effects of age, residency duration, and socio-economic factors on language attitudes among Mongolian Canadians, examining how these variables influence the construction of their identity in various social settings. The study uses quantitative analysis of survey data collected from 30 first-generation Mongolian immigrants in Canada, representing 2% of the total Mongolian diaspora population. The survey examines language attitudes across multiple domains: social solidarity, occupation, education, media consumption, and domain-specific usage. For example, the data reveals a clear pattern where older immigrants maintain stronger connections to their heritage language, particularly in home and social settings, while younger immigrants prefer English (see Table 6.1). Results reveal three key findings: First, the participants exhibit what Fishman (1977) terms "folk bilingualism," where the Mongolian language exists alongside English as a minority language with lower social status but a strong cultural connection. Second, unlike Polish-Canadians studied by Lustanski (2009), who view their mother tongue as less critical when not threatened in their homeland, Mongolian-Canadians maintain a strong attachment to their mother tongue despite its minority status. Third, similar to Canagarajah's (2013) findings with Tamil families, Mongolian-Canadians demonstrate fluid identity construction, with 43% of families using both languages in parent-child communication, as well as 54% of them reporting using both languages with friends.
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.
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 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".