Linguistic Preservation in the Digital Age: Anglicisms in French and Quebec IT Terminology – A Survey of IT Students
Bibliographic record
Abstract
This study explores the prevalence and integration of Anglicisms in French and Quebec IT terminology, analysing the tension between global linguistic influences and local preservation efforts. Through a two-phase survey involving 68 French-speaking computer science students, the research assesses participants’ familiarity with official French IT terms recommended by FranceTerme and their preferences compared to English alternatives. Key terms, including courriel (email), hameçonnage (phishing), and vignette active (widget), are examined alongside their Quebec-coined counterparts to highlight regional linguistic variations. The findings reveal significant challenges in the adoption of French equivalents, largely stemming from the comparative appeal and communicative efficiency of English terms. However, successful integration of terms like télécharger (download) and pare-feu (firewall) underscores the importance of conceptual clarity and cultural resonance. This study provides valuable insights into the effectiveness of linguistic policies in maintaining linguistic identity within a rapidly evolving field. It calls for collaborative approaches to terminology standardisation across Francophone regions to balance linguistic preservation and practical communication, ensuring that official policies are evaluated not only in terms of linguistic outcomes but also their practical relevance in rapidly evolving fields.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".