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Record W1602868702 · doi:10.26034/tranel.2001.2553

Particules métadiscursives et autres modes langagières: des cas de changement linguistique

2001· article· en· W1602868702 on OpenAlexaffabout
Diane Vincent, Guylaine Martel

Bibliographic record

VenueTravaux neuchâtelois de linguistique · 2001
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCharacter (mathematics)LinguisticsFrenchSociologyHistoryHumanitiesPhilosophyMathematics

Abstract

fetched live from OpenAlex

The present article focuses on the use of metadiscursive expressions and discourse particles that are produced in great numbers by Montreal speakers in different time periods and that have generally been regarded as language ticks. Elements of the first group make explicit the conscious state of speakers with respect to their language production; elements of the second group illustrate the fleeting and unstable character of language modes. In analyzing the elements of both groups, we cover the two aspects of the problem before us: addressing the question of language awareness and showing the evolution of discourse particles which are an indication of this awareness. The data on which our observations are made are taken from three sociolinguistic corpora created successively over time with the same Montreal francophone speakers: the Sankoff-Cedergen corpus (1971), the 1984 Montreal corpus and the Montreal 1995 corpus. Our analysis reveals that while both the frequency and the choice of particles vary with time, all of the speakers produce them, and that their production increases with age, regardless of sex or socioprofessional status.

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)0.002
metaresearch head score (Gemma)0.008
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.942

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0050.007
Scholarly communication0.0050.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.048
GPT teacher head0.336
Teacher spread0.289 · 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

Citations4
Published2001
Admission routes2
Has abstractyes

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