Searching for standard French: The construction and mining of the <i>Recueil historique des grammaires du français</i>
Bibliographic record
Abstract
Abstract This paper describes a massive project to characterize “Standard French” by constructing and mining the Recueil historique des grammaires du français (RHGF), a corpus of grammars whose prescriptive dictates we interpret as representing the evolution of the standard over five centuries. Its originality lies in the possibility it affords to ascertain the existence of prior variability, date it, and determine the conditions under which grammarians accept or condemn variant uses. Systematic meta-analyses of the RHGF reveal that grammarians rarely acknowledge the existence of alternate ways of expressing the same thing. Instead, they adopt three major strategies to establish form-function symmetry. All involve partitioning competing variants across distinct social, semantic or linguistic contexts, despite pervasive disagreement over which variant to associate with which. This effectively factors out variability. In contrast, systematic analysis of actual language use , as instantiated in the spontaneous speech of 323 speakers of Quebec French over an apparent-time period of a century and a half, reveals robust variability, regularly conditioned by contextual elements which have never been acknowledged by grammarians. This conditioning has remained largely stable since at least the mid-nineteenth century. Taken together, these results indicate that the “rules” for variant selection promulgated by grammarians do not inform the spoken language, nor do grammars take account of the variable rules structuring spontaneous speech. As a result, grammar and usage are evolving independently.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".