An extension of the comparative sociolinguistics approach for sociosyntax: Comparing a single linguistic constraint across multiple sociolinguistic variables
Bibliographic record
Abstract
This paper integrates aspects of both generative theory and variationist sociolinguistics. To compare the structure of two varieties of French (Acadian French and Laurentian French), I adapt the comparative sociolinguistics approach to compare the syntactic structure of these varieties. Specifically, I focus on the effects of a single linguistic constraint across multiple sociolinguistic variables. I argue that such a comparison provides insights into the underlying grammatical structures of the varieties under comparison, differences that may have remained hidden otherwise. To illustrate the approach, I focus on a single constraint, sentential polarity, and I analyze its effects on two sociolinguistic variables, yes/no questions and future temporal reference. Results show that the polarity constraint is operative in Laurentian French for both variables, but inoperative in Acadian French. To account for this difference, I argue that Laurentian French negative structures involve a negative head above the tense phrase while Acadian French does not.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".