Cross-linguistic structural priming of innovations in Canadian French
Bibliographic record
Abstract
Abstract Intra-individual language contact in bilinguals is considered a potential source for the emergence of structural innovations in a language, eventually leading to grammatical language change. This study investigates the psycholinguistic mechanisms involved in this process, focusing on cross-linguistic structural innovation priming. In a web-based self-paced reading experiment with production pre- and posttests, we tested Canadian French–English bilinguals on innovative French ditransitive and monotransitive structures primed by English sentences with the same structure or by control primes. No priming effect emerged for monotransitives. For ditransitives, however, reading times in the segment immediately following the innovation were significantly faster when primed by the corresponding English structure. In production, the proportion of innovative sentences did not significantly increase from pretest to posttest for either structure. Yet, production rates of innovative forms in both tasks were modulated by the individual degree of French contact. We discuss these differential outcomes with reference to theoretical accounts of the psycholinguistics of contact-induced change. Overall, these findings suggest that cross-linguistic priming can provide a pathway for structural innovations to enter bilingual grammars, potentially leading to language change. However, such processes are apparently constrained by the linguistic properties of the respective structure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".