When a linguistic variable doesn’t vary (much): The subjunctive mood in a conservative variety of Acadian French and its relevance to the actuation problem
Bibliographic record
Abstract
Abstract This study considers the subjunctive mood in one of the most conservative varieties of Acadian French, that spoken in the Baie Sainte-Marie region in the Canadian province of Nova Scotia. A number of claims made in the literature are considered: whether the subjunctive mood is undergoing loss, whether it expresses semantic meaning, and whether it is lexically-conditioned. Unlike most spoken varieties of French where the subjunctive is argued to be a linguistic variable (i.e., it varies with other moods), the results for Baie Sainte-Marie show that it varies very little. The analysis reveals that the few cases of variation can be accounted for by formal theoretical approaches to the subjunctive where this mood is argued to express modality. With limited variation, the subjunctive is not showing signs of loss. These findings suggest that the subjunctive is not part of a linguistic variable and so is not subject to inherent variability. I further argue that the retention of the imperfect subjunctive in this variety, along with a tense concordance effect, can help us understand why the subjunctive became a linguistic variable in other varieties of French, which ultimately contributes to our understanding of the actuation problem.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".