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Record W4367185797 · doi:10.3765/plsa.v8i1.5531

Community and lifespan changes in music: Sociophonetic variation in Laurentian French

2023· article· en· W4367185797 on OpenAlexaboutno aff
Kaitlyn Owens, Jeffrey Lamontagne

Bibliographic record

VenueProceedings of the Linguistic Society of America · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsVariation (astronomy)MusicalSingingVowelAppealHistoryLinguisticsArtLiteraturePolitical scienceLaw

Abstract

fetched live from OpenAlex

Previous studies on English have highlighted various instances where individual singers or small groups change which dialectal features appear in their music (e.g. Trudgill 1997; Beal 2009; Coupland 2011; Eberhardt & Freeman 2015; Lyon 2019). Whereas corpus studies on music have the option between real-time or apparent-time analyses, most previous research on music has largely been conducted via case studies on change across a singer or group’s career (see Gibson in press a). Focusing on Laurentian French (also known as Quebec French or Canadian French), multiple singers may moderate dialectal traits in music due to their albums being released internationally, where the dialect faces stigma (Szlezák 2015). We further hypothesize that pop singers are especially sensitive to international norms and stigma because they are more likely to market abroad due to pop music’s greater international appeal (Grenier 1993). We examine non-lengthened high vowel laxing in closed final syllables (e.g. /vit/ [vɪt] vite ‘fast’; Dumas 1983), a process characteristic of Laurentian French that is categorical (Côté 2012) and nonstigmatized (Lappin 1982; Paradis & Dolbec 1998; Reinke et al. 2006) within the dialect. We expand the study of dialectal traits in music beyond English by using a new corpus of 20 Québécois singers who sing in French and are of the Laurentian French dialect. Additionally, we analyze the patterning of groups of singers across their careers, rather than the patterning of a single singer, and analyze real-time change of groups as opposed to individuals

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)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.788
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.030
GPT teacher head0.290
Teacher spread0.259 · 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 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Linguistic Society of AmericaSame topicLinguistic Variation and MorphologyFrench-language works237,207