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Interpersonal Dynamics of Forgiveness

2016· article· en· W2796228727 on OpenAlexaffabout

Bibliographic record

VenueAcademy of Management Proceedings · 2016
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsForgivenessScholarshipContext (archaeology)Prosocial behaviorSocial psychologyPhenomenonPsychologyInterpersonal communicationHarmEpistemologyPolitical science

Abstract

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Although emerging scholarship has highlighted the potential benefits of workplace forgiveness, most of the research to date has failed to capture the complexity of this dynamic phenomenon; scholars typically construe forgiveness as a relatively simple prosocial victim sentiment, a passive response in reaction to the severity of the transgression and the appropriate amends of the harm-doer. In contrast, the current symposium examines forgiveness as a complex process that evolves as a result of the dynamic interplay between the transgression context (e.g., features of the offense, nature of the work context, third-party involvement) and the individuals involved (e.g., victim’s disposition, offender’s disposition, and the nature of the relationship between them). Individual victims are also not passive recipients of apologetic acts, but motivated actors who engage with their offenders to achieve specific psychological goals. Five forgiveness scholars from research teams across the globe (USA, Canada, the UK, Australia and Germany) will present their most recent empirical findings relevant to understanding forgiveness as an unfolding and dynamic process. Specifically, the research presented attempts to better capture the complex interplay between the victim, the offender, the relationship between them, and the importance of the embedded context. By tackling this complexity, all five papers expand our understanding of forgiveness, illustrating the interactions that can occur between these various factors. Following the presentations, the audience is engaged in an interactive discussion, with particular emphasis on gaining greater conceptual coherence within forgiveness research and promoting future scholarship that can better capture the complexity of the forgiveness phenomenon. Coping with Leader Social Undermining: The Role of Employee Forgiveness Presenter: Ryan Fehr; U. of Washington, Seattle Presenter: Payal Nangia Sharma; The Wharton School, U. of Pennsylvania Impediments to Forgiveness: Victim and Transgressor Attributions of Intent and Guilt Presenter: Gabrielle Adams; London Business School Presenter: M. Ena Inesi; London Business School Forgiveness as Revenge in Disguise? Offering Forgiveness to Offenders Diminishes their Moral Status Presenter: Tyler G. Okimoto; U. of Queensland Presenter: Mario Gollwitzer; Philipps U. of Marburg The Effect of Social Power and Apology on Victims' Post-Transgression Responses Presenter: Ward Struthers; York U. Presenter: Careen Khoury; York U. Presenter: Elizabeth Van Monsjou; York U. Presenter: Joshua Robert Guilfoyle; York U. Enhancing Trust and Forgiveness via Shame Displays: A Social Functional Perspective Presenter: Ivona Hideg; Wilfrid Laurier U. Presenter: Laurie J. Barclay; Wilfrid Laurier U. Presenter: Teodora Makaji; Wilfrid Laurier U.

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)0.004
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0070.011
Scholarly communication0.0090.007
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.299
Teacher spread0.281 · 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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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