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Record W4384203692 · doi:10.1017/s0025100323000154

Regional variation in articulation rate in French spoken in Canada

2023· article· en· W4384203692 on OpenAlexafffundabout
Władysław Cichocki, Svetlana Kaminskaïa, Luke Hagar

Bibliographic record

VenueJournal of the International Phonetic Association · 2023
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsActuaUniversity of WaterlooUniversity of New Brunswick
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsArticulation (sociology)WindsorVariation (astronomy)Reading (process)LinguisticsPlace of articulationSpoken languageStyle (visual arts)PsychologyGeographyPolitical scienceConsonantVowelBiologyPhilosophy

Abstract

fetched live from OpenAlex

This study examines articulation rate in three varieties of Canadian French and includes consideration of speaking style (reading vs. spontaneous), speaker’s age and gender, and length of inter-pause intervals. The varieties are spoken in different geographic areas of Canada – Quebec City (Quebec), Tracadie (New Brunswick), and Windsor (Ontario) – where there are different degrees of French–English contact. The main research question asks how these different contact situations are related to variation in articulation rate. Results show that in both reading and spontaneous speech articulation rates were faster among Quebec City speakers, where French is in a low-contact setting, and slower among speakers from Tracadie and Windsor, where there are greater degrees of contact. The effects of other factors are the same across the three regions: AR was faster in spontaneous productions than in reading; AR decreased with age in the reading task; AR was faster as the length of the inter-pausal intervals increased. The discussion points to similarities and differences with varieties of French spoken in Europe and underscores the importance of language contact in accounting for variation in articulation rate.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.299
Teacher spread0.277 · 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 designObservational
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

Citations2
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueJournal of the International Phonetic AssociationSame topicPhonetics and Phonology ResearchFrench-language works237,207