A Comparative Variationist Approach to Morphosyntactic Variation in Hexagonal and Quebec French
Bibliographic record
Abstract
French spoken in Quebec (QF) is largely considered to be vastly different from Hexagonal French (HF), spoken in France, but this assumption has not been tested empirically. Systematic and accountable analyses of morphosyntactic features in HF are generally absent from the current body of linguistic research on French, which precludes the possibility of reliable comparison. This dissertation addresses this gap through the systematic analysis of three different linguistic variables in spoken HF (the subjunctive mood, the expression of necessity, and the expression of pronominal indefiniteness), using the comparative variationist sociolinguistics framework, which seeks to account for variability in language by demonstrating that choices made by speakers are not random, but rather are constrained by various social and linguistic factors. The analysis tests whether the strong community trends that deviate from the prescribed standard, shown to be operative in QF (e.g., Poplack, Lealess & Dion 2013), are also present in the source variety (HF) and if so, whether the underlying conditioning of the variability is parallel across the two varieties. The results of this research reveal nearly identical underlying systems in both varieties in the case of the subjunctive mood and the expression of necessity. The most notable difference is the circumstantial proportion of specific lexical items (namely falloir que, which is strongly entrenched with the subjunctive mood in QF and is also the main variant used to express necessity), which is eventually linked to the different strategies employed in each variety to express indefinite necessity and indefiniteness more generally. The results of this research highlight the importance of considering the consequences of one change on another and how variability in one area of the grammar contributes to the larger picture. It also represents the first comparative variationist analysis of morphosyntactic variation in QF and HF, and thus contributes to clarifying our understanding of the (dis)similarities between the two varieties, in addition to offering insight into the relationship between transplanted dialects and their source variety more generally.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".