<i>J’va share mon étude sur les anglicismes avec vous autres!</i>: A sociolinguistic approach to the use of morphologically unintegrated English-origin verbs in Quebec French
Bibliographic record
Abstract
ABSTRACT This study explores variation in the use of English-origin verbs in Quebec French. These lexical borrowings are usually integrated grammatically into the receiving language (Poplack, 2018), as inil vacrasherandelle m’aghostéin Quebec French. However, a new lexical insertion strategy for English-origin verbs has been observed in the past few years: verbal borrowings can lack overt morphological integration, as inil vacrashandelle m’aghost.This article examines the use of English-origin verbs in Quebec French from a variationist perspective by focusing on 1) possible correlations between speakers and how they evaluate the different lexical insertion strategies, and 2) the social factors that constrain the use of morphologically unintegrated English-origin verbs. Results from quantitative analyses based on 675 participants indicate that young Quebecers from Montreal with a high level of proficiency in English are the ones who use this morphologically unintegrated form the most and evaluate it more positively. This unintegrated form poses a theoretical problem according to Poplack’s (2018) theory, for which nonce borrowings are morphologically and syntactically integrated into the receiving language.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 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".