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Record W1994012767 · doi:10.1016/j.jesp.2015.05.001

Perpetrator groups can enhance their moral self-image by accepting their own intergroup apologies

2015· article· en· W1994012767 on OpenAlexaff
Fiona Kate Barlow, Michael Thai, Michael J. A. Wohl, Sarah White, Marie-Ann Wright, Matthew J. Hornsey

Bibliographic record

VenueJournal of Experimental Social Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologySocial psychologySelf-image

Abstract

fetched live from OpenAlex

There is an implicit assumption that perpetrators' moral image restoration following an intergroup apology depends on absolution from victims. In this paper we examine whether perpetrators can in fact look to other ingroup members for moral pardon. In Studies 1 and 4, Australians read an apology to Indian people for a series of assaults on Indian nationals in Australia. In Studies 2 and 3, non-Aboriginal Australians were provided with apologies offered on their behalf to Aboriginal Australians. In each study participants were told that other perpetrator group members had either accepted or rejected the apology. In line with predictions, when perpetrator group members heard that fellow perpetrators accepted an apology made to victims they felt morally restored, and consequently were more willing to reconcile. Effects were largely unqualified by apology quality (Studies 2–4), and held in the face of victim group apology rejection (Studies 3–4). We demonstrate that perpetrator group members can effectively gain moral redemption by accepting their own apologies, even qualified ones that have proved insufficient to victim groups.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.385
Teacher spread0.339 · 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.

Study designBench or experimental
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

Citations28
Published2015
Admission routes1
Has abstractno

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