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Record W2766925490 · doi:10.5539/ells.v7n4p58

A Study on Culture Adaptation Difference between Chinese and American Advertising Discourses

2017· article· en· W2766925490 on OpenAlexvenueno aff
Jinlan Zhu

Bibliographic record

VenueEnglish Language and Literature Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
FundersSocial Science Foundation of Jiangsu ProvinceGovernment of Jiangsu Province
KeywordsAdvertisingCollectivismIndividualismAdaptation (eye)BusinessSociologyMarketingPolitical sciencePsychology

Abstract

fetched live from OpenAlex

Advertising discourse is a kind of strategic communicative language, and adaptation is often used as an effective strategy by advertisers to publicize the products and lure the customers to buy the advertised products. The study is conducted to compare the differences of culture adaptation in Chinese and American advertising discourse, finding different orientations where Chinese advertising discourses and American ones respectively adapt to the potential customers: 1. Collectivism vs. Individualism in Standard of Value; 2. Monism vs. Pluralism in Thinking Mode; 3. Authority vs. Equality in Concept; 4. Past Orientation vs. Future Orientation in Culture Tradition; 5. Implicity vs. Superiority in Culture Characteristics. The results will provide valuable implication and reference for the companies, advertisers and researchers, helping the advertisers design advertisements in line with the target culture and overcome the culture barrier to international advertising communication.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
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 head0.021
GPT teacher head0.323
Teacher spread0.302 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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