L'inclusion des Particularismes Extra-Hexagonaux dans la Derniere Edition du Grand Robert : Realite ou Mirage de la Francophonie ?
Bibliographic record
Abstract
Since the 1970s, French dictionaries have opened up to the French-speaking world, notably by including a number of words and usages characteristic of the many non-Metropolitan varieties of French. Tentative at first, this opening up has intensified with each re-edition to the point that today dictionaries compiled in France can give the impression that they reflect the entirety of French as used throughout the francophonie. This is the case, for instance, with the new Grand Robert de la langue française, which was received and presented in this way by reviewers in the Québec media. Using the introductory note of the two editions and the overhaul that the Grand Robert has undergone to date, this article first briefly recalls how the dictionary's interest in non-Metropolitan features came about and developed over time, then examines the conception of the French language that the compilers have come to adopt. On the basis of an analysis of a random sample of the nomenclature, which includes a number of Québecisms, this conception is then compared with the lexicographical practices adopted in the work as a whole. Finally, it shows how, as things stand at present, the opening up of French dictionaries to the French-speaking world can have the perverse effect of producing and maintaining a linguistic mirage.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".