Grammatical convergence or microvariation? Subject doubling in English in a French dominant town
Bibliographic record
Abstract
In French, subject doubling is “quite common” (e.g. Nadasdi 1995, Auger 1998, Thibault 1983, Zahler 2014) but in English it is rare (Southard & Muller 1998). Yet when anglophones speak French, they use subject doubling with French patterns (Nagy et al. 2003). In this paper, we analyze subject doubling in English in a bilingual French-English town. Usinga large corpus and statistical modelling, we show that thereis no difference between language groups, and neither sex, education nor job type are significant. The nature of the subject is the major predictor of doubling and there is a significant decrease among middle-aged speakers, suggesting mid-life social pressures on vernacular norms. Although subject doubling is low frequency, it is not stable across generations in the different language origin groups. While subject doubling may be a feature of vernacular dialects more generally, involving marking focus or topic marking as reported in other languages, in Kapuskasing when anglophones use it, they are accommodating to French patterns.
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How this classification was reachedexpand
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".