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Record W1991793717 · doi:10.1017/s0267190509090072

ASSESSING WORLD ENGLISHES

2009· article· en· W1991793717 on OpenAlexaboutno aff
Alan Davies

Bibliographic record

VenueAnnual Review of Applied Linguistics · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsWorld EnglishesVarieties of EnglishNorm (philosophy)Standard EnglishLingua francaHegemonyVariety (cybernetics)English-based creole languagesLinguisticsAmerican EnglishEnglish as a lingua francaEnglish languageSociologyLocal languageBritish EnglishPolitical scienceLanguage assessmentComputer scienceModern languageLawArtificial intelligence

Abstract

fetched live from OpenAlex

English worldwide may be viewed in terms of spread and of diffusion. Spread refers to the use in different global contexts, such as publishing and examinations, of Standard British or American English. Diffusion describes the emergence of local varieties of English in, for example, India or Singapore, comparable to the earlier emergence of Australian English, Canadian English, and so on. In nonformal settings, interlocutors make use of their own local variety of English, their World Englishes (WEs). In formal settings, notably in English language assessment, it seems that the norm appealed to is still that of Standard British or American English. Since English as a lingua franca (ELF) appears to make use only of the spoken medium, there is less of a demand for an ELF written norm. At present what seems to hold back the use of local WEs norms in formal assessment is less the hegemony of Western postcolonial and economic power and more the uncertainty of local stakeholders.

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)0.007
metaresearch head score (Gemma)0.030
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: Review · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0010.002
Scholarly communication0.0060.009
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.023
GPT teacher head0.296
Teacher spread0.274 · 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
GenreReview

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

Citations47
Published2009
Admission routes1
Has abstractyes

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