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Record W4388426329 · doi:10.1017/s0954394523000236

Subject dislocation in Ontario English: Insights from sociolinguistic typology

2023· article· en· W4388426329 on OpenAlexaffabout
Sali A. Tagliamonte, Bridget L. Jankowski

Bibliographic record

VenueLanguage Variation and Change · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTypologySubject (documents)LinguisticsSociocultural evolutionNorm (philosophy)VernacularNeuroscience of multilingualismDislocationSociologyHistoryPARRYAnthropologyEpistemologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract Subject dislocation (SD) is common across languages. In French, it is a vernacular norm. In English, it is comparatively rare. This article examines English SD in a unique contrastive situation in Ontario, Canada: two communities where SD is a community norm, one where individuals speak both English and French (Kapuskasing), and the other where the population speaks English only (Parry Sound). Dislocated subjects are produced by the same underlying linguistic mechanisms in both places, with parallel constraints by type of subject and intervening material, suggesting a typological universal. However, SD is age-graded in Kapuskasing, regardless of heritage language. In Parry Sound, it is obsolescent, in steady decline over the twentieth century. We conclude that while typological trends are underlain by universal cognitive processes, locally embedded sociocultural influences are the source of differentiation.

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)0.006
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 score0.057
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.007
Science and technology studies0.0090.013
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.302
Teacher spread0.258 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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