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Record W4407873514 · doi:10.31234/osf.io/wk9dg_v2

A new perspective on the development of Quebec French rhotic vowels

2025· preprint· en· W4407873514 on OpenAlexaboutno aff
Massimo Lipari, Morgan Sonderegger

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)LinguisticsComputer sciencePhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

Quebec French is reportedly developing rhoticity, with low F3 resulting from a bunched or retroflexed tongue (like English /ɚ/), in some or all of its front mid rounded vowels /ø, œ, œ̃/. The source of this rare, understudied sound change is unclear from previous work: contact with English and contrast enhancement have been suggested, and phonologization of coarticulation is typologically plausible. We examine this issue, investigating the apparent time change in the F3 trajectories of the three vowels using generalized additive mixed models on a corpus of parliamentary speech (106 speakers from across Quebec). We observe rhoticity in /ø/ and /œ̃ /: men begin with low F3 in these vowels, and women show change in progress. Conversely, there is less clear evidence of change in /œ/. We suggest these findings are best explained by rhoticity being a two-phased change, originally due to borrowing and subsequently spreading through contrast enhancement. Rhotacization, we argue, is the combined product of intensive exposure to English (which led to frequent non-integration of bunched/retroflex segments in loanwords) and an exceptionally large vowel inventory. It thus results from the unique interplay of social and phonological factors in Quebec French, which is consistent with such changes being cross-linguistically rare.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.042
GPT teacher head0.350
Teacher spread0.307 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same topicLinguistic Variation and MorphologyFrench-language works237,207