Metalinguistic Comments and Language Attitudes in the Novel Pour sûr by France Daigle
Bibliographic record
Abstract
France Daigle’s novel Pour sûr offers insights into Chiac, a variety of Acadian French spoken in Moncton, New Brunswick-Canada’s only officially bilingual province. Chiac, a unique North American “mixed” language involving French and English, is prominent in southeastern New Brunswick, where French coexists with dominant English. In this region, two-thirds of the population are English speakers, while one-third speak French. In Pour sûr , standard French is used for the narrative voice, while Chiac appears in dialogues, lending authenticity to the story. The novel explores Acadian history, culture, and the vernacular, including English borrowings, older linguistic features, specific conjugations, and hybridity. The text also reflects attitudes toward Chiac, often stigmatized as “corrupted French.” This chapter examines the metalinguistic comments within the novel’s dialogues and omniscient narrative voice. While many linguistic observations align with studies on Chiac, some discrepancies and errors appear, despite the authoritative tone of the narrator. The narrative’s attitudes reflect broader ideologies of linguistic standardization, which are often critical of Chiac. These perspectives align with sociolinguistic studies highlighting, within francophone minority contexts, ambivalence and harsh judgments of Chiac.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".