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Record W4285385038 · doi:10.1017/9781108864183.013

The Diverging Paths of Consequence Markers in Canadian French

2022· book-chapter· en· W4285385038 on OpenAlexaboutno aff
Hélène Blondeau, Raymond Mougeon, Mireille Tremblay

Bibliographic record

VenueCambridge University Press eBooks · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsDivergence (linguistics)FrenchVernacularGenealogyHistoryFacet (psychology)GeographyLinguisticsHumanitiesLiteratureArtPsychologyArchaeologyPhilosophySocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.993
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.225
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

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