What can be learned about the grammar of French from corpora of French spoken outside France
Bibliographic record
Abstract
This paper looks at some questions which were considered quite differently before corpora became the ordinary way to describe languages, focusing on the following points: a) Our ultimate objective is to document how wide-reaching the appellation “French” can be, given the extent of variation found in the corpora: is it possible to document the whole variational span of “the French language”, in what Chaudenson (2003: 182) would call “the limits of intra-linguistic variability of French”? b) Are there grammatical phenomena which could be looked at differently and analysed using corpora? c) Is it possible to generalise in an explanatory perspective, and to determine something of the principles which lie behind the difference between standards and vernaculars? d) The discussion of ordinary and non-standard data, mostly spoken (only occasionally written) will lead me to consider if it is possible to qualify vernacular varieties as such, in what Chambers called in 2000 “universal sources of the vernacular” and in 2003 “vernacular roots”; and what I shall choose to call here “vernacular re- source” (see the concluding remarks in section 3). The linguistic variation data which will be looked at in sections 1 and 2 are primarily diatopic, and occasionally diastratic: they mostly come from geographically “periphercal” French (mostly North American) and socially “marginal” French, which means here ways of speaking which have been subjected to / been the target of (albeit sometimes in a limited way) normative pressures, as is the case for ordinary spoken French, “popular French”, youth language, child language, and different types of urban or rural vernaculars.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".