Bibliographic record
Abstract
Several studies have analyzed sociolinguistic variation in Quebec French (QF) vernaculars, but few have examined more careful QF speech. This paper examines verbal negation and the variable use of the negative clitic ne in the speech of 32 members of Quebec’s cultural elites during recent (2003–2011) televised sit-down interviews. As a subset of our sample is interviewed in two different settings, one which deals with emotional personal narratives (Un Train corpus) and another in which speakers talk about a more objective topic (Le Point corpus, see Bigot 2008), the comparison between corpora further assesses the status of the negative particle as a stylistic marker. For instance, our analysis of both corpora reveals rates of ne use far superior to those observed in QF vernaculars, as well as a significant effect of address pronoun (formal 2s vous or informal tu) and age. We also show that operative linguistic constraints in our careful QF data are similar to those described in the literature on colloquial French (e.g. effect of collocations and subject type), and remain stable across speaker groups and interview settings. These results indicate that although speakers are aiming towards an elusive ‘standard Quebec French’ (SQF), they are constrained by a cohesive mental grammar even in careful speech. In short, this study fills a gap in the literature by using comparative sociolinguistics methods to provide a more nuanced description of verbal negation in ‘standard Quebec French’ (SQF), one which measures the relative effect of social, stylistic and linguistic factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| 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".