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Record W2071036831 · doi:10.1080/15534510.2012.674745

The role of right wing authoritarianism on the repentance–forgiveness process

2012· article· en· W2071036831 on OpenAlexaff
Careen Khoury, C. Ward Struthers, Alexander G. Santelli, Zdravko Marjanovic

Bibliographic record

VenueSocial Influence · 2012
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsQueen's UniversityYork University
Fundersnot available
KeywordsRepentanceForgivenessPsychologySocial psychologyAuthoritarianismTheologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

The purpose of this research was to examine the role of right wing authoritarianism (RWA; Altemeyer, 1996) and social norms, or expectations of proper social conduct, in the repentance–forgiveness process. Two studies systematically revealed a negative relation between RWA and forgiveness, a positive effect of repentance on forgiveness, and a significant interaction between RWA and repentance on forgiveness. The interaction, in both studies, showed that there was a stronger positive relation between repentance and forgiveness for those who were high in RWA compared to those who were low in RWA. The results of Study 2 also showed that the interaction between RWA and repentance on forgiveness was explained by the victim's perception of the transgressor's response as proper. These results help to further establish the boundary conditions under which repentance influences forgiveness and the mechanisms underlying its effects.

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)0.006
metaresearch head score (Gemma)0.017
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.320
Teacher spread0.306 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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