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Record W3040325737 · doi:10.1111/spc3.12554

Revenge as social interaction: Merging social psychological and interpersonal communication approaches to the study of vengeful behavior

2020· article· en· W3040325737 on OpenAlexaff
Susan D. Boon, Stephen M. Yoshimura

Bibliographic record

VenueSocial and Personality Psychology Compass · 2020
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConceptualizationInterpersonal communicationPsychologyPerspective (graphical)Social exchange theorySocial psychologySocial relationInterpersonal interactionProcess (computing)Interpersonal relationshipCognitive scienceEpistemologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The purpose of this essay is to review and assess the benefits of merging social psychological and communication theory‐based approaches to the study of vengeful behavior in interpersonal interactions. We first outline the parallel but complementary perspectives that each discipline takes to the conceptualization of revenge. From there, we identify some of the core features that would be present in an integrated approach that conceptualizes revenge as an interpersonal process (i.e., an interaction or exchange), and then highlight new directions for both inquiry and theory building that an integrative approach reveals as worthy of scholarly pursuit. We argue that conceptualizing and studying revenge in ways that blend both social psychological and communication‐based views offers numerous opportunities to examine the dynamics between a provoking party and an avenger, and provides a richer and more insightful theoretical understanding of vengeful behavior than either perspective could offer alone.

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)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.003
Science and technology studies0.0020.027
Scholarly communication0.0100.011
Open science0.0010.004
Research integrity0.0030.004
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 head0.353
GPT teacher head0.437
Teacher spread0.084 · 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 designTheoretical or conceptual
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

Citations21
Published2020
Admission routes1
Has abstractyes

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