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Record W4387183501 · doi:10.1002/ejsp.3006

Tensions between collective‐self forgiveness and political repair

2023· article· en· W4387183501 on OpenAlexaff
Michael Wenzel, Blake Quinney, Michael J. A. Wohl, Anna M. Barron, Lydia Woodyatt

Bibliographic record

VenueEuropean Journal of Social Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsCarleton University
Fundersnot available
KeywordsForgivenessCollective identitySocial psychologyPsychologyPoliticsIndigenousCollective responsibilityIdentity (music)State (computer science)EmpowermentPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Faced with collective guilt, perpetrator groups may seek collective‐self forgiveness. However, does this diminish their support for political repair? Advancing the concept of collective‐self forgiveness, we distinguish between end‐state collective‐self forgiveness as restored moral identity and two processes: pseudo collective‐self forgiveness as defensive downplaying and genuine collective‐self forgiveness as ‘working through’ the ingroup's guilt. In three studies, non‐Indigenous Australians ( N = 369, 800 and 785) were surveyed about currently debated constitutional changes for the recognition and empowerment of Indigenous Australians. Pseudo and genuine collective‐self forgiveness were positively related to end‐state collective‐self forgiveness. Pseudo and end‐state were negative, but genuine collective‐self forgiveness positively, related to support for repair and truth telling. Participants identifying with both Australians and Indigenous Australians more strongly endorsed genuine collective‐self forgiveness. The results suggest a pathway for perpetrator group members to balance identity needs with commitment to repair, but highlight drawbacks of seeing collective‐self forgiveness as an end‐state objective.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.059
GPT teacher head0.378
Teacher spread0.319 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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