Altruism born of suffering among emerging adults in Northern Ireland
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore altruism born of suffering (ABS), a theory that explains how the experience of suffering within one’s own life may result in the motivation to help others, even outgroup members. Design/methodology/approach Participants were 186 emerging adults (63 per cent female, 37 per cent male; 69 per cent Protestant, 41 per cent Catholic; average age =21.3, SD=2.57 years old) in Northern Ireland, a setting of protracted intergroup conflict. Participants were randomly assigned to an in/outgroup condition, read four types of adversity that occurred to same-sex victim(s), and indicated their empathetic response and how much they would like to help the victims. Findings Moderated mediation analyses revealed that empathy for the victim partially mediated the impact of perceived harm on desire to help; moreover, recent negative life events strengthened the link between harm and empathy. The path between empathy and helping was stronger in the outgroup compared to the ingroup condition. Practical implications These findings support ABS, highlighting empathy as a key factor underlying more constructive intergroup relations in a divided society. Originality/value This paper extends previous research on ABS by focusing on a post-accord context. The value of the current analyses demonstrate the important role of fostering empathy to promote outgroup helping in settings of divisive group identities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".