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Record W2626347865 · doi:10.1108/jacpr-01-2017-0271

Altruism born of suffering among emerging adults in Northern Ireland

2017· article· en· W2626347865 on OpenAlexfundno aff
Laura K. Taylor, Jeffrey R. Hanna

Bibliographic record

VenueJournal of Aggression Conflict and Peace Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsEmpathyPsychologyHarmOutgroupAltruism (biology)Social psychologyIngroups and outgroupsContext (archaeology)Prosocial behaviorMediationDevelopmental psychologyValue (mathematics)Sociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.464
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2017
Admission routes1
Has abstractyes

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