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Record W1987446183 · doi:10.1353/lan.2003.0171

<b>The vocabulary of world English</b> . By Stephan Gramley. London: Arnold, 2002. Pp. xiv, 323. ISBN: 0340740728. $27.50.

2003· article· en· W1987446183 on OpenAlexaboutno aff
Alan S. Kaye

Bibliographic record

VenueLanguage · 2003
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsInstitutionLexisVocabularyClassicsHistoryLiberal arts educationSociologyThe artsMedia studiesLinguisticsHigher educationPolitical scienceLawPhilosophySocial science

Abstract

fetched live from OpenAlex

Reviewed by: The vocabulary of world English by Stephan Gramley Alan S. Kaye The vocabulary of world English. By Stephan Gramley. London: Arnold, 2002. Pp. xiv, 323. ISBN: 0340740728. $27.50. This book is another splendid addition to the series The English language edited by David Britain and Rebecca Clift. It is a fascinating treatise on lexis which is intended to serve as a lexicology textbook. The many topics covered include English in the British Isles, the US, West Africa, and South Asia; English for special/specific purposes; and so-called internet English (IE). Students will appreciate the examples used to explain dialectal differences. Let us consider the British and American usage of ‘college’. For the author, Senior Lecturer in English at the University of Bielefeld, ‘a college is first and foremost a small, private, all-male, denominational liberal arts institution with a distinct regional basis’ (41). For Americans, the word ‘college’ is often synonymous with ‘university’, as in ‘I want to go to college when I finish my stint in the military’, or it may refer to a unit within a university, such as the College of Humanities and Social Sciences at the institution where I teach. Further, no American thinks of a college as an all-male institution. A particularly fine illustration of American usage is drawn from its antonomastic terminology: ‘your John Hancock’, ‘an Uncle Tom like him’, ‘he’s a Benedict Arnold’ (36). Interestingly enough, many of these terms are known and used outside the US and may be considered borrowings. A bit awkward and certainly confusing for me, the term ‘American English’ (298) is used to cover Canadian English as well (i.e. North American); however, the author also makes specific reference to Canadian English (300). One of the work’s strengths is the delightful discussion of English vocabulary from around the world. Let us consider some Pakistani English numerals that are not well-known internationally (57): lakh is 100,000; arab is 1 billion; kharab is 10,000 crore (crore = ten million < Hindi krōr), and so on. Familiarity with West African English would include next tomorrow ‘day after tomorrow’ (a calque from Yoruba [136]) and with Indian English, black money ‘illegal gains’ or change-room ‘dressing room’ (137). Singapore English is shown to have, not surprisingly, many borrowings from Malay and varieties of Chinese and Indian languages (138). Mention of IE is scattered throughout the volume. Many of the eighteen abbreviations cited for IE are becoming more and more common although some of these can be traced back to pre-internet days (277), for example, AFAIK ‘as far as I know’, GIGO ‘garbage in, garbage out’ (going back to the early days of software). One term, TTFN (‘ta-ta for now’) originated in a BBC radio program during World War II called ITMA (‘It’s That Man Again’), featuring comedian Tommy Handley. Perhaps better examples of IE acronyms include H and K ‘hug and kiss’ and LOL ‘laughing out loud’. Finally, the author mentions data from what he terms ‘Southern Hemisphere English’. One example from Australian English, Abo (clipping of Aborigine), is said to be ‘often derogatory’ (258). From my experience, the word ‘often’ may be excised. As far as I know, like the term boong which also designates an indigenous Australian, it is always derogatory. According to the Cambridge international dictionary of English (Cambridge: Cambridge University Press, 1995, p. 3), ‘the word is generally considered offensive’. I think the word ‘generally’ may be omitted. This is a delightful book and word nerds will particularly enjoy perusing it. Alan S. Kaye California State University, Fullerton Copyright © 2003 Linguistic Society of America

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)0.001
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.163
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0020.002
Scholarly communication0.0060.010
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1630.144

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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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