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Record W4414432096 · doi:10.7146/hjlcb.vi65.152910

Linguistic Preservation in the Digital Age: Anglicisms in French and Quebec IT Terminology – A Survey of IT Students

2025· article· en· W4414432096 on OpenAlexaboutno aff
Pavla Zubkova, Jan Lazar

Bibliographic record

VenueHERMES - Journal of Language and Communication in Business · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsTerminologyFrenchRelevance (law)CLARITYAppealVignetteIdentity (music)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.300
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueHERMES - Journal of Language and Communication in BusinessSame topicLinguistics, Language Diversity, and IdentityFrench-language works237,207