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The effects of third‐party validation and minimization on judgments of the transgressor and the third party

2011· article· en· W1543531153 on OpenAlex
Judy Eaton

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBritish Journal of Social Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsWilfrid Laurier University
FundersWilfrid Laurier University
KeywordsForgivenessThird partyPsychologySocial psychologyInterpersonal communicationMarine transgressionInterpersonal relationshipInternet privacyComputer science

Abstract

fetched live from OpenAlex

Victims of interpersonal transgressions often turn to friends, family, and trusted others when trying to make sense of negative events. This research explored the effect of two of the many ways that these informal third parties can respond: validating the victim's experience, and downplaying or minimizing the transgression. Two studies found that validation from a third party increases revenge motivations and that minimization of the transgression is more effective than validation at reducing revenge motivations. However, results also indicated that victims judge third parties more positively if they validate rather than minimize the transgression. These findings suggest that, when choosing between validation and minimization, third parties must make a choice between promoting forgiveness and promoting themselves.

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.

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 score0.881
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.300
Teacher spread0.279 · 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