French loanwords in Quebec English: Bilingualism, language proficiency and intraregional variation
Bibliographic record
Abstract
The article examines the issues associated with the difference in the relative frequency of French loanwords in Quebec English in Montreal and other cities of this Canadian province. The existing studies note that the loanwords with fewer occurrences are more widely used outside Montreal, the largest and most bilingual city in Quebec. The study illustrates this phenomenon with regard to the use of French loanwords in X (formerly Twitter) publications for the period from 2014 to 2024. It provides statistics on the use of various French loanwords in Montreal, as well as in the other major cities of the province: Quebec City, Saguenay, Gatineau, Sherbrooke and Trois-Rivières. The research seeks to explain this counterintuitive observation, as bilingualism is generally thought to facilitate and encourage borrowing. To this end, the paper discusses the nature of bilingualism in Quebec, its historical and cultural origins, as well as the geographical and demographic boundaries for the different levels of bilingualism and the English language proficiency in the province. The study concludes that less frequent French loanwords are relatively more widely used in Quebec outside Montreal because bilinguals there, speaking almost exclusively French in everyday life, tend to have it as their dominant language and therefore rely on French as a mediator when expressing concepts in English.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".