Assimilation of Gallicisms in the Canadian English-language media as a reflection of the sociolinguistic situation in the country
Bibliographic record
Abstract
This article analyzes the issues related to the identification of the main tendencies in the process of assimilation of French loanwords into Canadian English. It highlights sociolinguistic peculiarities of Canada, which has a long history of coexistence between the two languages, and provides a classification of loanword assimilation types, as well as some graphic and morphological assimilation subtypes characteristic of Canadian English. Based on the content analysis of the most relevant materials published since 2012 in the Canadian English-language media, both national and regional (The Montreal Gazette, The Toronto Star, CBC, The Suburban), as well as those published in the American media since 2020 (The New York Times, New York Post, USA Today, The Washington Post), statistics on the frequency of use of assimilated and non-assimilated forms of Gallicisms are provided. By measuring and comparing their frequency, the article reveals the tendency of Canadian English to preserve the French norm regarding both graphic and morphological assimilation of loanwords. The study cites authentic contextual examples of the use of assimilated and non-assimilated Gallicisms in the Canadian English-language media. It concludes that the identified patterns are directly linked to the specific features of Canada’s sociolinguistic situation, with its ever-growing bilingual population, and that further research in this field remains relevant in view of the country’s ongoing sociolinguistic changes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".