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Record W2756341827 · doi:10.1075/eww.38.2.03lev

A big city perspective on<i>come/came</i>variation

2017· article· en· W2756341827 on OpenAlexaff
Stephen Levey, Susan Patricia Fox, Laura Kastronic

Bibliographic record

VenueEnglish World-Wide A Journal of Varieties of English · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of Ottawa
FundersEconomic and Social Research Council
KeywordsVariation (astronomy)ForegroundingEthnic groupAlternation (linguistics)GrammarContrast (vision)LinguisticsPerspective (graphical)HistoryDemographyGenealogySociologyGeographyAnthropologyArt

Abstract

fetched live from OpenAlex

Abstract This study examines the alternation between non-standard preterite come and its standard counterpart came in London English. A major component of the investigation centers on the comparison of come/came variation in the speech of Anglo (British-heritage) and non-Anglo (migrant-heritage) youth. Rates of preterite come vary markedly across different age cohorts and minority ethnic groups, foregrounding the importance of social factors as key determinants of variant use. By contrast, the internal conditioning of variant selection is not robust, as inferred from the paucity of significant linguistic effects. Similarities in variable patterning in elderly and adolescent Anglo speaker groups nevertheless suggest that shared structural affinities may be due to historical transmission. Conversely, comparison of Anglo and non-Anglo adolescents’ use of come/came variation reveals fewer correspondences in the grammar underlying variable use. The results demonstrate that data from non-Anglo groups contribute to a fuller understanding of come/came variation in an ethnically diverse metropolis.

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)0.139
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.139
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.311
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 teacher head, not a consensus.

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

Citations6
Published2017
Admission routes1
Has abstractyes

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