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Record W7135309535

Verbal Negation and Quebec Cultural Elites

2017· report· W7135309535 on OpenAlexaboutno aff
Anne-José Villeneuve

Bibliographic record

VenueScholarlyCommons (University of Pennsylvania) · 2017
Typereport
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNegationCliticVariation (astronomy)SociolinguisticsPronounSubject (documents)Personal pronounNarrativeGrammar
DOInot available

Abstract

fetched live from OpenAlex

Several studies have analyzed sociolinguistic variation in Quebec French (QF) vernaculars, but few have examined more careful QF speech. This paper examines verbal negation and the variable use of the negative clitic ne in the speech of 32 members of Quebec’s cultural elites during recent (2003–2011) televised sit-down interviews. As a subset of our sample is interviewed in two different settings, one which deals with emotional personal narratives (Un Train corpus) and another in which speakers talk about a more objective topic (Le Point corpus, see Bigot 2008), the comparison between corpora further assesses the status of the negative particle as a stylistic marker. For instance, our analysis of both corpora reveals rates of ne use far superior to those observed in QF vernaculars, as well as a significant effect of address pronoun (formal 2s vous or informal tu) and age. We also show that operative linguistic constraints in our careful QF data are similar to those described in the literature on colloquial French (e.g. effect of collocations and subject type), and remain stable across speaker groups and interview settings. These results indicate that although speakers are aiming towards an elusive ‘standard Quebec French’ (SQF), they are constrained by a cohesive mental grammar even in careful speech. In short, this study fills a gap in the literature by using comparative sociolinguistics methods to provide a more nuanced description of verbal negation in ‘standard Quebec French’ (SQF), one which measures the relative effect of social, stylistic and linguistic factors.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0060.005
Scholarly communication0.0020.015
Open science0.0040.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.003

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 head0.045
GPT teacher head0.278
Teacher spread0.233 · 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; both teacher heads agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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Same venueScholarlyCommons (University of Pennsylvania)French-language works237,207