Using comparative sociolinguistics to inform European minority language policies: Evidence from contemporary Picard and regional French
Bibliographic record
Abstract
Abstract We argue that an evaluation of morphosyntactic convergence between Picard and French must consider multiple variables, comparing rates of (co-)occurrence of Picard-like and French-like variants and linguistic constraints across the two varieties. Contemporary oral data from interviews with Picard–French bilinguals and French monolinguals were analyzed and contrasted with older Picard data. While future temporal reference in Picard and in French appear similar based on frequency, linguistic conditioning reveals differences across varieties and over time. Auxiliary selection displays clearer Picard–French distinctions, especially when considering the effect of linguistic factors. The intersection of variables shows that the differences between Picard and French are qualitative and not simply quantitative. In the context of the debate over the status of Northern France's obsolescent varieties, we provide empirical evidence for a mental grammar in Picard distinct from that of French, and show the relevance of comparative sociolinguistics for language planning.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".