MétaCan
Menu
Back to cohort
Record W1857029392

Comparative Sociolinguistic Insights in the Evolution of Negation

2015· article· en· W1857029392 on OpenAlexaboutno aff
Claire Childs, Christopher D. Harvey, Karen P. Corrigan, Sali A. Tagliamonte

Bibliographic record

VenueScholarly Commons (University of Pennsylvania) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
FundersEconomic and Social Research CouncilArts and Humanities Research Council
KeywordsNegationLinguisticsPrestigeVariation (astronomy)MarkednessPsychologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

There are three ways of expressing negation on indefinites in English: any-negation (I didn’t have any money), no-negation (I had no money) and negative concord (I didn’t have no money). These variants have been competing diachronically in a change in progress, where the newest variant any-negation is increasing at the expense of the oldest variant no-negation (Tottie 1991a, 1999b, Varela Pérez 2014). This raises the questions: What is the current state of this variability? Is the variation socially evaluated? What does this reveal about linguistic change? Our comparative quantitative sociolinguistic analysis of vernacular speech corpora from Northern England and Ontario, Canada reveals that no-negation is stoutly retained in Britain but is less frequent in Canada. Linguistic constraints on the variation hold cross-dialectally: functional verbs retain no-negation, while lexical verbs favour any. However, the social embedding of the variation is community-specific. Where the change to any-negation is more advanced, i.e., Canada, there are no significant social effects: the variation between any-negation and no-negation appears stable. In England, where no-negation is conserved to a greater extent, there are effects of speaker sex and education, with men and less-educated speakers favouring no-negation. Furthermore, both of the UK communities (North East England and York) display age-grading trends which suggest that the prestige associated with any-negation historically has persisted over time. While the communities share a common variable grammar, the social value in choosing a variant is localised and reflects the status of the change.

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)0.001
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 score0.579
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.078
GPT teacher head0.307
Teacher spread0.229 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueScholarly Commons (University of Pennsylvania)Same topicLinguistic Variation and MorphologyFrench-language works237,207