Bibliographic record
Abstract
This article is the first quantitative investigation of future temporal reference in spoken Hexagonal French. The two variants under examination are the inflected future (e.g. je partirai ‘I will leave’) and periphrastic future (e.g. je vais partir ‘I am going to leave’). The present study will determine the distribution of future verb forms in Hexagonal French and investigate whether the constraint systems reported for varieties of Canadian French also hold in a European context. By contrasting variable usage with Canadian speech communities, this paper contributes to our understanding of the linguistic factors that unite and divide la francophonie. It also adds a French perspective to the existing literature on global linguistic trends. Results suggest that the strategies of encoding future time in Hexagonal French mirror to a certain degree the findings reported in the extant Canadian literature. Chi-square and fixed/mixed-effects logistic regression models furthermore highlight the complex set of constraints governing the expression of future temporal reference in mainland France. Crucially, they indicate that the inflected form is still highly productive, with a frequency distribution comparable to the conservative Acadian French varieties. Nevertheless, the constraint hierarchy patterns like Laurentian French, with sentential polarity identified as the greatest determinant of variant choice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".