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Record W4405918425 · doi:10.1080/23311983.2024.2442828

Exploring taboo culture: a cross-cultural analysis of taboos in China and Malaysia

2024· article· en· W4405918425 on OpenAlexaff
Yu Junxuan, Zaiton Zakarya, Salinah Ja’afar, Kamaruzaman Jusoff, Norisma Idris

Bibliographic record

VenueCogent Arts and Humanities · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSwearing, Euphemism, Multilingualism
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsTabooMalayIntercultural communicationCross-cultural communicationChinaCross-culturalSociologyCultural diversityNarrativePsychologySocial psychologyQualitative researchPolitical scienceSocial scienceLinguisticsCommunicationAnthropology

Abstract

fetched live from OpenAlex

Taboos occur in intercultural communication and can sometimes lead to misunderstandings, obstacles, and conflicts in cross-cultural communication. The objective of this study was to compare the similarities and differences between the Chinese and Malay taboo cultures. The study used qualitative research method, and the design was based on Giles’s Communication Accommodation Theory using the narratives research approach. The findings show that many differences exist in the taboos of Chinese and Malay communities in terms of lifestyle, such as food, gift-giving, and greetings. It is concluded that strengthening cultural exchange could lead to better intercultural communication and cooperation towards achieving mutual tolerance and understanding. The findings imply that understanding taboos will create awareness and strengthen international relations between the two countries. Future studies can focus on other linguistic elements.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.164
GPT teacher head0.373
Teacher spread0.209 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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