Bibliographic record
Abstract
Humans make mistakes, and as a result, apologies are an inescapable aspect of intercultural communication. This paper suggests that cultural pragmatics are the foundation for an effective apology. Through a content analysis of sources, the key contextual factors that impact an apology are individualism-collectivism orientations, rooted in the social values of different cultures. Some of the key findings proposed that these different orientations are exemplified in Japanese and American cultures, as they tend to focus on either the group or the individual in an apologetic situation. Apologies are not cross-culturally universal, but based on the pragmatics of cultural orientations, especially individualism-collectivism, they can be predicted. To examine this in the paper, apologies are defined in the context of universality, and Japan/the US are identified as cultures that present strong social contexts, requiring cultural context to create an apology. Then, the literature review establishes the importance of these socially based expectations through linguistics, social purpose, and saving face. The discussion section then argues that these concerns are more important than situational cues, that an individualistic orientation is less complicated to predict in regard to apologies, and that these pragmatic preparations prevent the escalation of the act being apologized for. In the conclusion, it is pointed out that even with these contextual clues, apologies are not entirely predictable, but these tools can help mitigate cultural misgivings.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".