The frequency and distribution of written and spoken anglicisms in two varieties of French
Bibliographic record
Abstract
This study examines the frequency and distribution of anglicisms in written and spoken French using a corpus of over 100,000 words collected from two reality television shows and from blogs - data representing two varieties of French: Quebecois French (QC), and French from France (FR). The following research questions guided this study: (1) Which variety of French uses a higher total percentage of anglicisms? (2) Which language mode (written or oral) is characterized by a higher frequency of anglicisms? (3) How does the distribution of different anglicism categories (Wholesale, Direct Translations, Hybrids, and French Inventions and Modifications) compare across French language varieties? The results indicate that, overall, anglicisms tend to make up less than one percent of the corpus (0.99%) when using a token analysis, and 2.80% when analyzing anglicism types. Furthermore, of this total, the percentage of tokens/types in FR was 0.94% / 2.80%, while QC totaled 1.03% / 2.80%. Concerning language mode, anglicisms also appear to be equally frequent in the spoken (TV programs) and written (Internet blogs) corpora for both tokens and types. However, when taking language variety into consideration, FR uses a higher percentage of anglicisms in writing, while QC employs more anglicisms in spoken language. Finally, distribution results suggest that while FR and QC share the preference for anglicizing most frequently within the Wholesale and Hybrid categories, the two language varieties differ in the distribution of anglicisms among the Direct. Translation, and French Invention and Modification categories
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".