Sociolinguistics, regional varieties of French and regional languages in France
Bibliographic record
Abstract
May I at the outset crave the reader's indulgence for focusing the subject matter very largely on metropolitan France. The regional varieties of French referred to in the title are therefore those spoken in the French regions, rather than, for example Belgian or Canadian varieties. Moreover, while it is impossible to discuss the regional languages question without taking into account the languages of the DOM-TOM and, indeed, the so-called non-territorial varieties, both of which have taken on considerable political significance in recent times, I have largely limited myself to reviewing sociolinguistic studies of ‘metropolitan’ regional (i.e. territorial) languages. I have also decided to concentrate on the present and thus may be perceived as giving short shrift to the large and growing body of excellent socio-historical work in the field. Four major approaches are reviewed: firstly, the work inspired by the dialectological tradition on French regionalisms (section 2); secondly, quantitative variationist studies (section 3); thirdly, the Imaginaire Linguistique approach to linguistic perceptions (section 4) and fourthly, the approach emerging from the notion of diglossia, as defined by Catalan and Occitan linguists (section 5). Sections 6 to 8 deal with current issues – the Poignant (1998), Carcassonne (1998) and Cerquiglini (1999) reports and the vitality of regional languages as presented in numerous surveys of largely professed practices and exposure in the audio-visual media.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".