Expressing Refusals in English: A Cross-Cultural Study of Invitation Responses amongst Malaysians
Bibliographic record
Abstract
Malaysia is a multicultural country with diverse cultural groups, languages, social practices and norms. The Malays, Indians and Chinese are amongst the leading cultural groups, each with unique styles of expressing refusal when communicating in English. Refusal, or the act of saying ‘no,’ is inevitable in daily routines, often leaving a negative impression on both speaker and listener. This can be particularly challenging in multicultural context, whereby different cultures use language differently to express and interpret refusals in English. Such variations can lead to misunderstandings, especially in multilingual settings. This study examined how different cultural perspectives in Malaysia expressed refusals to invitations in English. To realise the study, a qualitative approach complemented by minor quantitative elements was adopted to provide an insightful understanding of the refusal strategies used by different cultures. The employed research design included Oral Discourse Completion Task (ODCT) and interviews. A purposive sampling technique was applied to select 16 Malays, 16 Chinese and 16 Indians who are proficient English speakers. The data obtained were analysed by using Beebe et al. (1990) Refusal Taxonomies framework to categorise refusal strategies and Hofstede (2011) Cultural Dimensions to interprete an in-depth insight into cultural influences in making refusals. The findings revealed that Chinese and Indians were comfortable to express direct negative willingness in English, while Malays tended to refuse indirectly, often showing gratitude in their refusals. Future research is recommended to explore refusal strategies in other speech acts and compare refusal styles between working adults and children in Malaysia to enhance generalisability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".