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Record W3044885408 · doi:10.5539/elt.v13n8p152

Tuhaos with Hongbaos are Going to the English World: Study on the Features of Chinese English Neologisms Based on Web

2020· article· en· W3044885408 on OpenAlexvenueno aff
Zongwei Song

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsnot available
FundersSichuan University
KeywordsNeologismThe InternetChinaVariety (cybernetics)LinguisticsEnglish languageInflectionPsychologyHistoryWorld Wide WebComputer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

This article discusses the features, reasons, and values of the mushrooming Chinese English neologisms (CENs). Generally speaking, CENs are a variety of English words, namely Chinese English words, some of which have entered Oxford English Dictionary (OED). Based on data from Web Corp Live, the author finds that: (1) CENs take on the grammatical and morphological characteristics of English words, such as inflection and derivation, which are not found in the previous related studies; (2) CENs belong specific semantic domains, which are closely related to China’s latest social, economic, cultural development in the network era. CENs are the production of language contact between Chinese and English in the time of Internet. CENs possess important values to observe or understand Chinese new social phenomena and to promote the communications between the Chinese and the English world.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.232
Teacher spread0.218 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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