Phrase-final prepositions in Quebec French: An empirical study of contact, code-switching and resistance to convergence
Bibliographic record
Abstract
In this study, we investigate whether preposition stranding, a stereotypical non-standard feature of North American French, results from convergence with English, and the role of bilingual code-switchers in its adoption and diffusion. Establishing strict criteria for the validation of contact-induced change, we make use of the comparative variationist framework, first to situate stranding with respect to the other options for preposition placement with which it coexists in the host language grammar, and then to confront the variable constraints on stranding across source and host languages, contact and pre-contact stages of the host language, mainstream and “bilingual” varieties of the source language, and copious and sparse code-switchers. Detailed comparison with a superficially similar pre-existing native language construction also enables us to assess the possibility of a language-internal model for preposition stranding. Systematic quantitative analyses turned up several lines of evidence militating against the interpretation of convergence. Most compelling are the findings that the conditions giving rise to stranding in French are the same as those operating to produce the native strategy, while none of them are operative in the presumed source. Explicit comparison of copious vs. sparse code-switchers revealed no difference between them, refuting claims that the former are agents of convergence. Results confirm that surface similarities may mask deeper differences, a crucial finding for the study of contact-induced change.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".