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Record W2790378493 · doi:10.3765/plsa.v3i1.4306

What motivates high vowel deletion in Québec French: Foot structure or tonal profile?

2018· article· en· W2790378493 on OpenAlexafffundabout
Natália Brambatti Guzzo, Heather Goad, Guilherme D. Garcia

Bibliographic record

VenueProceedings of the Linguistic Society of America · 2018
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDeterminerSyllableLinguisticsVowelNoun phraseNounDeterminer phraseHead (geology)Tone (literature)MathematicsComputer scienceSpeech recognitionPhilosophy

Abstract

fetched live from OpenAlex

Previous studies have argued that high vowel deletion (HVD) in Québec French is constrained by iterative iambic footing (Guzzo, Goad & Garcia 2016, Garcia, Goad & Guzzo 2017; see also Verluyten 1982), since it preferentially applies in even-numbered syllables from the right edge of the word. In this paper, we compare this hypothesis with an alternative hypothesis: HVD is constrained by the optionally-realized phrase-initial H tone (Jun & Fougeron 2000, Thibault & Ouellet 1996). We report on a judgement task in which two- and four-syllable nouns with HVD in the initial syllable are placed in phrases of different profiles (No determiner, Determiner + noun, Determiner + adjective + noun). If tonal profile plays a role in HVD, HVD in four-syllable nouns in phrases where the noun is in isolation or preceded by a determiner alone should be dispreferred, since the initial syllable of the noun is assigned the optional H tone in these contexts. Our results do not confirm this: HVD is favored in four-syllable nouns over two-syllable nouns, regardless of phrase type. We explain this finding by expanding our previous proposal: HVD is regulated by foot structure, but is dispreferred when it targets the head foot (where the obligatory phrase-final prominence is realized).

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.308
Teacher spread0.287 · 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

Citations3
Published2018
Admission routes3
Has abstractyes

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Same venueProceedings of the Linguistic Society of AmericaSame topicPhonetics and Phonology ResearchFrench-language works237,207