The Diverging Paths of Consequence Markers in Canadian French
Bibliographic record
Abstract
This study provides a real–time analysis of variation in the use of consequence markers (ça) fait (que), donc, alors and English borrowing so in two genetically related varieties of Canadian French. It is based on corpora collected in the 1970s and 2010s in Montreal, Quebec, a majority francophone environment, and Welland, Ontario, a minority francophone environment. Comparison of the two corpora reveals that Montreal and Welland French had already started to diverge in the 1970s in relation to variant inventory, variant frequency, and constraints on their use and that intercommunity divergence has intensified over time. Among the manifestations of divergence, one can mention the emergence of connector so in Welland in the 1970s and its subsequent growth, at the expense of vernacular variant (ça) fait (que) . This stands in contrast with a marked increase of (ça) fait (que) and its diffusion to all social groups in Montreal over time. The evolution of standard variant alors reveals another facet of intercommunity divergence. In Montreal, it has undergone a sharp decline and is becoming obsolescent in the speech of the younger generations; however, in Welland, it evidences stability. Our study discusses some of the (extra)linguistic factors accounting for such patterns of divergence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".