Bibliographic record
Abstract
The purpose of this study is to analyze the phenomenon known as “anglicism”: a loan made to the English language by another language. Anglicism arose either from the adoption of an English word as a result of a translation defect despite the existence of an equivalent term in the language of the speaker, or from a wrong translation, as a word-by-word translation. Said phenomenon is very common nowadays and most languages of the world including making use of some linguistic concepts such as anglicism, neologism, syntax, morphology etc, this article addresses various aspects related to Anglicisms in French through a bibliographic study: the definition of Anglicism, the origin of Anglicisms in French and the current situation, the areas most affected by Anglicism, the different categories of Anglicism, the difference between French Anglicism in France and French-speaking Canada, the attitude of French-speaking society towards to the Anglicisms and their efforts to stop this phenomenon. The study shows that the areas affected are, among others, trade, travel, parliamentary and judicial institutions, sports, rail, industrial production and most recently film, industrial production, sport, oil industry, information technology, science and technology. Various initiatives have been implemented either by public institutions or by individuals who share concerns about the increasingly felt threat of the omnipresence of Anglicism in everyday life.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".