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Record W2025119646 · doi:10.1177/1948550611425861

Awareness of Common Humanity Reduces Empathy and Heightens Expectations of Forgiveness for Temporally Distant Wrongdoing

2011· article· en· W2025119646 on OpenAlexaff
Katharine H. Greenaway, Winnifred R. Louis, Michael J. A. Wohl

Bibliographic record

VenueSocial Psychological and Personality Science · 2011
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsCarleton University
FundersUniversity of Queensland
KeywordsForgivenessWrongdoingRemorseEmpathyPsychologyHumanitySocial psychologyTheologyEpistemology

Abstract

fetched live from OpenAlex

The present research identified why and under what conditions perpetrator groups expect forgiveness from victims when focused on common humanity. In Experiment 1 ( N = 41), thinking about victims as fellow humans increased expectations of forgiveness among perpetrator group members. Experiment 2 ( N = 74) revealed the important role of subjective temporal distance in qualifying the effect of appealing to common humanity. Forgiveness expectations increased when a transgression was perceived as temporally distant rather than close. Experiment 3 ( N = 70) found that expecting forgiveness was associated negatively with remorse for wrongdoing and revealed reduced empathy for victims as a mediator of the effect. Taken together, the findings reveal that factors shown to encourage forgiveness among victims can also create corresponding expectations among perpetrators. Unfortunately, this process is more likely to damage than repair intergroup relations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.175
GPT teacher head0.419
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2011
Admission routes1
Has abstractyes

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