First-person plural in Prince Edward Island Acadian French: The fate of the vernacular variant <i>je…ons</i>
Bibliographic record
Abstract
In Atlantic Canada Acadian communities, definite on is in competition with the traditional vernacular variant je…ons (e.g., on parle vs. je parlons “we speak”), with the latter variant stable only in isolated communities, but losing ground in communities in which there is substantial contact with external varieties of French. We analyze the distribution of the two variants in two Prince Edward Island communities that differ in terms of amount of such contact. The results of earlier studies of Acadian French are confirmed in that je…ons usage remains robust in the more isolated community but is much lower in the less isolated one. However, in the latter community, the declining variant, while accounting for less than 20% of tokens for the variable, has not faded away. Although it is not used at all by some speakers, it is actually the variant of choice for others, and for still other speakers, it has taken on a particular discourse function, that of indexing narration. Comparison with variation in the third-person plural, in which a traditional variant is also in competition with an external variant, shows that the decline of je…ons is linked to its greater saliency, making it a prime candidate for social reevaluation.An earlier version of this article was presented at UKLVC-3, held in July 2001 at the University of York, U.K. We thank audience members for comments. We also thank Raymond Mougeon, along with this journal's anonymous referees, for useful comments on an earlier written version. The research was funded by standard research grants awarded to King and Nadasdi by the Social Sciences and Humanities Research Council of Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".