The role of English in shaping contemporary French academic vocabulary: a sociolinguistic analysis
Bibliographic record
Abstract
This study investigates how English influences the formation of academic French vocabulary in the field of education across Francophone regions (France, Canada, Belgium, Switzerland, and West Africa). We review sociolinguistic and policy literature on lexical borrowing and language globalization and compile data on English‐derived terms and semantic anglicisms in educational discourse. Using a mixed-methods approach, we analyze examples of English borrowings (e.g. e-learning, MOOC, coaching, manager) drawn from curricula, academic publications and media, and compare usage across different Francophone contexts. We also consider attitudes toward anglicisms (e.g. survey data from Quebec students). Our results show that English has supplied numerous new terms in educational French, sometimes with adapted meanings. For instance, notions like e-learning (fr. formation en ligne) or manager (fr. gestionnaire) appear widely, reflecting globalized pedagogy. Semantic shifts are common (e.g. Fr. support in education vs. Eng. support). English borrowings remain pervasive despite language laws: France’s Toubon Law (1994) officially bans Anglicisms in legislation, yet English terms persist in academic contexts; in Québec, Bill 101 (1977) and Institut nounformulations (e.g. courriel for “email”) explicitly resist English influence. A literature review and corpus analysis (with sample data tables) reveal trends in English‐origin terminology, adaptations, and regional variation. We discuss how globalization drives lexical borrowing, and how language policy and community attitudes mediate the impact of English on French academic lexicon. Findings suggest that English borrowings in education are likely to grow, as scholars note they have become “necessary in modern French”. These insights inform language planners and educators about the dynamics of anglicisms in Francophone higher education.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".