Interpersonal Dynamics of Forgiveness
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".