From Words to Food – A Cross-Cultural Study of Parental Apologies and Forgiveness
Bibliographic record
Abstract
Although apologies are widely recognized as a tool for repairing relationships, little is known about how parents apologize to their children, especially across cultural contexts. In the present research, we examined if and how Chinese and Canadian parents apologize to their children after a wrongdoing, focusing on verbal vs. nonverbal apologies. In Study 1, we investigated university students who reported their parents’ apology tendencies, whereas in study 2, parents reported their own apology tendencies. The results were consistent across studies. Canadian parents were more likely than Chinese parents to apologize verbally to their children in real-life transgressions. In response to hypothetical transgression scenarios, Canadian parents again were more likely to offer verbal apologies, whereas Chinese parents were more likely to offer no apology or to apologize non-verbally. Different types of apologies also had culturally specific emotional and relational effects. Although students from both cultures were likely to forgive after a verbal apology, Chinese students were more forgiving towards non-verbal apologies compared to Canadian students. Chinese students also reported greater closeness to their parents after a non-verbal apology than did Canadian students. These findings provide a unique perspective on communication and conflict resolution patterns in parent-child relationships and highlight the importance of considering cultural context when studying the meaning and impact of an apology.
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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 | 0.004 | 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 | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".