The hidden dimensions of a change from below: Consequence markers in Montreal French
Bibliographic record
Abstract
Abstract This article examines the rise of vernacular consequence marker ça-fait-que (CFQ), often realized as [fɛk] or [fak], at the expense of its standard counterparts donc and alors in Montreal French. The apparent-time analysis is based on a 2012 corpus of semi-directed interviews collected in Montreal. Previous studies treated the CFQ/donc/alors alternation as a purely lexical sociolinguistic variable. Our analysis shows how a vernacular variant ( CFQ ), initially associated with the working class and stigmatized, comes to compete, develop as a default form, and eventually crowd out forms at the other end of the social prestige scale ( alors and donc ). We rely on new socio-phonetic considerations to unveil a reconfiguration of the variable. The integration of the sociophonetic dimension sheds light on a complex process of diffusion, where a change from below is propelled by an additional change, but from above. Our article shows the key role played by women in both changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".