Between French and English, Between Ethnography and Assimilation: Strategies for Translating Moncton’s Acadian Vernacular*
Bibliographic record
Abstract
Chiac, the hybrid vernacular spoken by Acadians in the Moncton region, is increasingly used in works of fiction. By placing it on a par with French, Acadian novelists attempt to legitimize it as the language of a modern and urban Acadie. Their task is a difficult one, to which they respond with ambivalence: Chiac inscribes a difference which marginalizes them, whereas its absence amounts to a disappearance into the French norm. As a consequence, writers using Chiac face the challenge of making room for hybridity without dissociating themselves from their francophone identity. In their encounter with Chiac, translators of Acadian literature into English face a challenge of their own. Both multilingualism and vernacular languages have been deemed untranslatable, and Chiac happens to be at once multilingual and a vernacular. The dilemma faced by these translators is hence not too far from the dilemma of writers of Chiac: how much difference should they erase, how much should they insist on it at the risk of confirming stereotypes? How can they assist and pursue attempts at legitimization? How can they avoid assimilation into English on the one hand, and ethnography on the other? This article investigates the strategies brought into play by two translators who have tackled Chiac and its ambivalent use by Acadian novelists: Robert Majzels, translator of France Daigle, and Jo-Anne Elder, translator of Gérald Leblanc. Keywords: Chiac, Acadian literature, Acadian literature in translation, literary multilingualism, sociolects and vernaculars.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".