"New Insights into Forgiveness and Mercy: Antecedents, Outcomes, and the Role of Third Parties"
Bibliographic record
Abstract
Over the past decade, scholarly interest in forgiveness and mercy in the workplace has been rapidly increasing. However, the literature is still in its infancy and more theoretical and empirical attention is critically needed to provide a deeper understanding of these phenomena and their implications in the workplace. The purpose of this symposium is to address key questions in this literature (e.g., what are the predictors and outcomes of forgiveness and mercy), challenge assumptions (e.g., whether forgiveness and mercy are always constructive responses), and expand our perspective to include contextual factors that can influence the processes underlying forgiveness and mercy (e.g., the effects of third parties). Drawing upon a variety of qualitative and quantitative methodologies as well as theoretical perspectives, the symposium brings together leading experts in this literature to shed light on pertinent research issues including: a) the antecedents and consequences of forgiveness and mercy in the workplace, b) the affective and psychological mechanisms that underlie the process of forgiveness and mercy as well as the relationship between these constructs and outcomes, c) moderators that impact the effectiveness of forgiveness, and d) the influence of third parties for forgiveness and mercy. In addition to addressing these critical research questions, the symposium will also include an interactive discussion aimed at highlighting key themes and future research avenues for research. When Do Observers Trust Forgiving Victims? Presenter: Lukas Neville; U. of Manitoba Beyond the Decision to Forgive: How Third Parties Can Influence the Aftermath of Forgiveness Presenter: Maria Francisca Saldanha; Wilfrid Laurier U. Presenter: Laurie J. Barclay; Wilfrid Laurier U. The Social Costs and Benefits of (Non)Forgiveness Presenter: Dena Gromet; The Wharton School, U. of Pennsylvania Presenter: Tyler G. Okimoto; U. of Queensland The Role of Causal Reasoning on Workplace Forgiveness Presenter: Sana Rizvi; U. of Waterloo Presenter: Ramona Bobocel; U. of Waterloo The Injustice of Granting Mercy: A Third Party Perspective Presenter: Marie S. Mitchell; U. of Georgia Presenter: Kate Zipay; U. of Georgia Presenter: Mike Baer; Arizona State U. Presenter: Robert Bies; Georgetown 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 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) | 0.000 | 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".