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Record W2972920300 · doi:10.1017/9781316827437.025

Regional Varieties of English

2019· book-chapter· en· W2972920300 on OpenAlexaff
Michael E. Adams

Bibliographic record

VenueCambridge University Press eBooks · 2019
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVariety (cybernetics)Standard EnglishVarieties of EnglishScope (computer science)American EnglishLinguisticsEnglish languageStandard languageHistoryBritish EnglishComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

For centuries now, English has been the language of villages and nations throughout the world, and it varies, we say, according to something we call ‘region’, though we could hardly come up with a vaguer term. The most familiar dictionaries of English describe a standard variety, though different regions may develop different standards over time – Webster’s Third New International Dictionary of the English Language accounts for Standard American English, while The Macquarie Dictionary does the same for Standard Australian English, and so on (for both, see Chapter 23). Some dictionaries of regional English represent a nation’s distinctive lexical features, while others represent local features, with dictionaries of every imaginable scope in between those extremes. ‘No nation is of a piece’, writes F. G. Cassidy – editor of both the Dictionary of American Regional English and the Dictionary of Jamaican English – ‘It is no accident therefore that language, which reflects conditions in the society, is nowhere all of a piece either.’ Regional dictionaries assemble non-standard pieces of English into complex pictures of regional language, history, and culture.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.728
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.032
GPT teacher head0.181
Teacher spread0.149 · 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
GenreOther

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

Citations4
Published2019
Admission routes1
Has abstractyes

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