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Record W4282939338 · doi:10.1111/weng.12585

Using VADIS to weigh competing epicentral influence

2022· article· en· W4282939338 on OpenAlexaboutno aff
Roberta La Peruta

Bibliographic record

VenueWorld Englishes · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsCocaSimilarity (geometry)Variety (cybernetics)American EnglishLinguisticsRelevance (law)Subject (documents)Variation (astronomy)Contrastive analysisHistoryCorpus linguisticsField (mathematics)Political scienceComputer scienceLawArtificial intelligenceArchaeologyPhilosophyMathematicsLibrary science

Abstract

fetched live from OpenAlex

Abstract The present study sets out to explore the mandative subjunctive in Canadian English (CanE), vs. its potential epicentre American English (AmE), and its historical input variety British English (BrE) based on a quantitative variationist analysis of the Strathy Corpus of Canadian English (Strathy), the Corpus of Contemporary American English (COCA), and the British National Corpus (BNC). The relevance of this contribution primarily stems from the fact that no previous research has yet focused on a contrastive comparison between CanE and its alleged epicentres, namely ‘a model of English for (neighbouring?) countries’ (Hundt, 2013, p. 185), and that the new method of Variation‐Based Distance and Similarity Modeling (VADIS) has so far never been applied to research in this field. Key findings show that VADIS is indeed a valuable method in detecting epicentral constellations, and pinpoint fruitful suggestions regarding AmE's alleged transnational influence on its neighbor, as well as cross‐border and transoceanic dis/similarities concerning the subject under analysis.

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)0.005
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.004
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.037
GPT teacher head0.321
Teacher spread0.284 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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