Texting the future in Belgium and Québec: Present matters
Bibliographic record
Abstract
Abstract This study investigates the variation in the expression of Future Temporal Reference in text messages in Belgian and Québécois French. Three variants are considered: the Futurate Present, the Synthetic Future and the Analytic Future. The results of multivariate analyses show that the use of the Futurate Present does not appear to be subject to dialectal variation: both communities use this variant at similar rates, and the use of the variant is constrained by the same linguistic factors. The two dialects show differences in their choice of the Synthetic vs the Analytic Future. Unlike Québécois French, Belgian French strongly favours the Synthetic Future. The two dialects also differ with respect to the linguistic constraints in effect. Our analysis shows the need to explore the relationship between variants, and to distinguish between Covert T (realized as Present tense) and Overt T (either Synthetic or Analytic Future). Our results point toward the hybrid nature of text messages: while our results show patterns of use in line with oral/conversational corpora as reflected by the dialectal variation observed, text messages are not exempt from the influence of written French, as shown by the use of Synthetic Future forms in affirmative sentences in the Québec corpus.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".