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Record W3083030474 · doi:10.1051/shsconf/20207802006

Le destin de ‹<i>-oir(e)</i>› en français laurentien et la neutralisation de l’opposition /ɑ/ ~ /ɔ/ devant /ʁ/

2020· article· fr· W3083030474 on OpenAlexaboutno aff
André Thibault

Bibliographic record

VenueSHS Web of Conferences · 2020
Typearticle
Languagefr
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

L’évolution [wε] &gt; [wa] de la diphtongue ‹ oi › est un phénomène encore inabouti en français laurentien, sensible à tous les axes de la variation linguistique ainsi qu’au contexte phonétique. Cet article est consacré à ‹ -oir(e) ›, la position de la diphtongue devant rhotique posant des problèmes phonétiques et phonologiques particuliers. Après un bref tour d’horizon du discours métalinguistique sur cette variable, on offrira une représentation cartographique de sa réalisation dans les parlers traditionnels et quelques données aréologiques plus récentes sur le français canadien contemporain, basées sur des données d’enquêtes en ligne. La seconde partie de l’exposé sera consacrée à un problème phonologique. La diphtongue /wɑ/ a un statut monophonématique en français laurentien. En effet, on observe – entre autres – que l’opposition /ɑ/ ~ /ɔ/ tend à être neutralisée devant /ʁ/ (part et port devenant homophones), alors que le second élément de la diphtongue d’un mot en ‹ -oir(e) › ne se confond pas avec le [ɔ] d’un mot en ‹ -or › et reste [ɑ]. Il se trouve toutefois que le système est peut-être en train de changer, comme l’analyse d’un corpus de chansons québécoises, ainsi que de récentes enquêtes en ligne, le suggèrent : [wɔʁ] pour ‹ -oir(e) › est désormais une réalisation possible.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.539

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.035
GPT teacher head0.279
Teacher spread0.244 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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Same venueSHS Web of ConferencesSame topicLinguistics and Discourse AnalysisFrench-language works237,207