‘chtileu qu'i m'freumereu m'bouque i n'est point coér au monne’: Grammatical variation and diglossia in Picardie
Bibliographic record
Abstract
ABSTRACT In this article, we analyze French and Picard data, extracted from sociolinguistic interviews with four Picard–French bilingual speakers and four French monolingual speakers from the Vimeu (Somme) area of France, in order to determine whether the two closely-related varieties maintain distinct grammars or whether they now constitute varieties of the same language. Focusing on two linguistic variables, subject doubling andnedeletion, we argue that the variation observed in our French data results from variation within a single grammar, while our Picard data display markedly different patterns that can only be explained by a speaker's switch to a Picard grammar. We propose a model that schematises our results and attempts to reconcile the notions of diglossia and variation. In addition to providing empirical evidence in favour of an approach that recognises the structurally distinct status of Picard, our data indicate that resorting to a diglossic approach for French fails to capture the intrinsically variable nature of human language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".