Francisation of Sponsor, Coach, and Start-up – the Perspective of the French Linguistic Policy
Bibliographic record
Abstract
The article concerns one of the important elements of the cultural policies implemented in France and Quebec, that is, their linguistic policies. Although they are two distinct, French-speaking territories on two sides of the Atlantic, their policies share some important elements. The article focuses on one of them – the approach to foreign terms, which today are mostly American terms, permeating into the French language and the French terms recommended by special governmental organisations in France and Quebec that are supposed to replace these foreign terms. After a brief outline of the two policies and general principles of francisation of foreign terms, the work-ings of the process of francisation are analysed. The object of analysis are terms recommended as equi-valents of three English terms from the area of management and marketing: sponsor, coach, and start-up. Adopting the linguistic point of view, it is shown that francisation involves a reference to many di-verse dimensions – involving both the structural, cultural, and conceptual level.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".