Positive and Negative Politeness: A Cross-Cultural Study of Responding to Apologies by British and Pakistani Speakers
Bibliographic record
Abstract
Speech etiquette is an essential part of culture, behavior and human communication. Based upon a theoretical framework of politeness and face-threatening acts (FTAs), this study investigates cultural differences in apology responses (ARs) moderated by the threatened face type and the relationship between participants. A discourse completion test, consists of twelve situations is used for data collection. The data was collected from 150 Pakistani Urdu speakers (teachers, doctors, army personals, lawyers, journalists and academicians) working in different institutions and 30 British English speakers (faculty members of English Department, Coventry University, UK, Leeds University, UK and British Association of Applied Linguistics members). The findings reveal that Pakistanis are found using more positive face threatening apology responses (Acceptance and Acknowledgment) including Absolution, Dismissal, Intensifiers, and Acknowledgement with Thanking, Advice, and Suggestion, than British speakers who tend to use both positive FTAs (Acceptance) based on Absolution “That’s Okay”, and Dismissal “no worries at all but be careful next time” and negative FTAs based on Evasion with Deflection and Evasion with Thanking. The findings further illustrate that the understanding and demonstration of politeness and face in conversation functions are susceptible to cultural and sociolinguistic variations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".