Apology Strategies in High vs. Low Context cultures
Bibliographic record
Abstract
Apologies play a crucial role in interpersonal relationships, However, (Blum-Kulka and Olshtain, 1987) mention that culture and the power between the apologizer and the person who was offended can affect the production of apologies. Thus, the present study investigates the impact of culture on apology strategies by comparing high and low context cultures, specifically Jordan and Canada, respectively. The research sample comprises 40 undergraduate students, with 20 Jordanian native Arabic speakers and 20 Canadian native English speakers. Data collection involved a written discourse competition questionnaire, which presented nine hypothetical apologetic scenarios, each representing different power dynamics between the apologizer and the offended party (high, equal, and low power). The questionnaire was translated into Arabic for Jordanian participants and distributed in English for the Canadian participants. Coding and analysis of the data employed frequencies and percentages to identify and quantify the usage of apology strategies by each cultural group. Furthermore, a chi-square test was conducted to examine differences in apology strategies between Jordanians and Canadians across high, low, and equal power relationships. The findings reveal that both cultural groups utilized six apology strategies, namely illocutionary force indicating device, promise of forbearance, offer of repair, explanation, concern for the hearer, and assessment of responsibility. Canadians exhibited consistent usage of apologies regardless of the power dynamics, which suggests that power did not affect how Canadians apologized. In contrast, Jordanians employed a significantly higher number of strategies when the person who was offended held a high-ranking position, but no differences were noticed when the addressee was in an equal or low-ranking position, which suggests that power affected how Jordanians apologized. Additionally, Jordanians used significantly more apology strategies compared to Canadians when apologizing to a person in a high-ranking position. On the other hand, Canadians used significantly more apology strategies when the person who was offended was at an equal or low power ranking position. The findings of the study were explained using the characteristics of high and low context cultures.
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 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 | 0.001 | 0.004 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".