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Record W1582284909 · doi:10.5539/ass.v11n15p211

The Pragmatics of Chinese Proverb Quoting in the English and the Russian-Language Mass Media of PRC

2015· article· en· W1582284909 on OpenAlexvenueno aff
O. V. Nikolaeva, Ekaterina A. Yakovleva

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
FundersFar Eastern Federal University
KeywordsLinguisticsPragmaticsForeign languageConnotationChinaPersuasionTarget cultureSource textMeaning (existential)SociologyPsychologyHistoryPhilosophy

Abstract

fetched live from OpenAlex

Mass media of PRC in languages others than Chinese native (in the present research English and Russian) are aform of cross-cultural communication between China and the rest of the world. In other-language mass mediaChina not only presents its views and attitude to the events described, but also reveals China “the whole self” byemploying the fragments of its traditional verbal culture, proverbs in particular being the ways of self-expression.However, the research provided evidence that the foreign-language press of China carefully considers theappropriateness of proverb quotations, and thoroughly estimates the degree of transmitted by them informal andindirect culture-based information, which the target audience is capable or incapable to grasp, share, and abideby. Proverbs generally being among the most indirect strategies of reasoning and persuasion in some contextualuse may convey straightforward and strict judgments. The paper studies significant theoretical issues andapplication aspects of proverb quoting in the foreign-language press of PRC which depends on crucial questionsof cross-cultural pragmatics: first, the addressor’s natural urge for culture-based self-expression alongside withstriving for intelligibility to the foreign target audience, and second, the addressor’s choice of higher / lowercontext in communication with different cultures-addressees. This accounts for the discrepancy betweenpresentation of information in the English-language press of PRC and the Russian-language periodical of China. Both questions suggest a wide range of options in proverb quoting (preserving/omitting a proverb, originalproverb with glossing / loan translation, meaning / connotation modulation of a proverb, etc).

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.301
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2015
Admission routes1
Has abstractyes

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