‘chtileu qu'i m'freumereu m'bouque i n'est point coér au monne’: Grammatical variation and diglossia in Picardie
Why this work is in the frame
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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 and ne deletion, 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it