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Record W2186100321

Assessing contact-induced language change: The use of subject relative markers in Quebec English ∗

2011· article· en· W2186100321 on OpenAlexaboutno aff
Allison V. Lealess, Chelsea T. Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamLanguage contactLinguisticsLanguage changeVarieties of EnglishVariety (cybernetics)Neuroscience of multilingualismAmerican EnglishConvergence (economics)Subject (documents)Multivariate statisticsEnglish languagePsychologyBritish EnglishComputer scienceMathematicsPolitical scienceStatisticsArtificial intelligenceLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

To investigate the relationship between language contact and language change, this pilot study undertakes a comparative variationist analysis of relativizers used in subject function in mainstream Canadian and Quebec English. Overall rate of who is the same in both varieties and in multivariate analyses neither variety nor bilingual ability is selected as significant to variant choice, arguing against convergence. Differences in the linguistic conditioning of who across varieties suggest that it is mainstream English, not Quebec English, which has changed. While a breakdown of variant use by sex and age initially suggests contact-induced change, a comparison of speaker cohorts across varieties and a consideration of speaker characteristics fail to confirm convergence. Contributing to the debate on the use of WH-relativizers in spoken English, this study demonstrates that who is a viable option in Canadian English. It also provides evidence against claims that bilingualism is correlated with susceptibility to convergence.

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)0.003
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 score0.646
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.162
GPT teacher head0.352
Teacher spread0.191 · 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

Citations35
Published2011
Admission routes1
Has abstractyes

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