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Record W2387879734

On Characteristics of Canadian English

2003· article· en· W2387879734 on OpenAlexaboutno aff
Hongwei Liu

Bibliographic record

VenueWuhan University Journal · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)LinguisticsPronunciationVocabularySyntaxPhenomenonBritish EnglishAmerican EnglishEnglish languageNational identityHistorySociologyComputer sciencePolitical scienceArtificial intelligencePolitics
DOInot available

Abstract

fetched live from OpenAlex

After hundreds of years’ development, Canadian English has become a true national language and has its national identity. Due to the historical and regional factors, most of the language features of Canadian English are found in either British English or American English, and sometimes in both. It is the particular combinations of these features that set apart Canadian English. While culturally and politically independent, Canadians have a very distinctive variety of English; different from Br. E and Am. E in terms of vocabulary, syntax, pronunciation, etc, Canadian English has developed into a unique variety of English. In addition, a lot of borrowings from other languages form another part of the unique Canadian vocabulary and reflect important aspects of the experience of the Canadian people. As a unique linguistic phenomenon as well as a national language, it is quite certain that Canadian English should develop in its own way.

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)0.008
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 score0.050
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.009
Science and technology studies0.0100.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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