Cross-Cultural Courtesy: An Examination of How Bengali Speakers and US English Speakers Differ in Politeness
Bibliographic record
Abstract
This study explores the nuances of politeness across two linguistic and cultural contexts: Bengali and US English. Politeness, a fundamental aspect of social interaction, is deeply influenced by cultural norms and values, shaping how individuals navigate relationships and maintain harmony. Drawing on Brown and Levinson’s politeness theory, the research investigates, through structured interviews assessing perceptions of politeness, how the principles of "face" are applied in these languages. The findings reveal significant cultural contrasts. Rooted in a collectivist culture, Bengali speakers often emphasize respect, deference, and indirectness, particularly when addressing elders or authority figures. In contrast, US English speakers, influenced by individualistic values, tend to prioritize directness and clarity, valuing equality and informal interaction. Additionally, power dynamics are crucial in shaping polite behavior among Bengali speakers but less so among US speakers. Politeness Theory highlights how power dynamics influence the strategies employed to maintain or threaten face, shaping perceptions of face-saving or face-losing acts. The results of this study reveal that cross-cultural interactions often lead to misunderstandings due to perceived face-threatening acts (FTAs). These misinterpretations stem from culturally influenced differences in linguistic expressions, perceived status and hierarchy, and contrasting cultural orientations, such as collectivism versus individualism.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".