Building on an old feature in<i>langue d’Oïl</i>: interrogatives in Vimeu Picard
Bibliographic record
Abstract
ABSTRACT Picard faces challenges in its quest for recognition, in part due to its perceived similarity with French. While scholars recognize that Picard and French phonology, morphology and lexicon differ considerably, some scholars maintain that Picard syntax differs little from French. Suspecting that such assessments are based on superficial comparisons, we test their validity by performing comparative variationist analyses of Picard and French morphosyntactic structures. This article focuses on interrogatives. We compare older and contemporary written data, as well as contemporary oral data, and show that Picard and French use their shared structures differently and that the Picard Yes/No interrogative system is complex but constrained by two linguistic factors: polarity and person. We report very different distributions of SV, inversion and interrogative – ti based on polarity and show that negative markers point and mie constrain the choice of interrogative structure. For affirmative interrogatives, we show that the distribution of interrogative structures is strongly constrained by the subject person. A diachronic analysis of text from nine authors from three generations reveals overall stability over time, with some signs of convergence toward French in the middle generation but a reversal to the older patterns in the youngest generation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".