Uncovering the Complexities of Forgiveness: Forgiveness Norms, Motives, and Types
Bibliographic record
Abstract
The importance of forgiveness for the modern workplace has increasingly been recognized in recent years. Despite the recent surge in research on the topic, there are still many important questions that need to be answered, which will provide a deeper understanding of forgiveness and its implications in organizations. The purpose of this symposium is to address key questions in this literature, including investigating what drives individuals' forgiveness, exploring what types of forgiveness-related responses exist, and understanding the consequences of different types of forgiveness. Drawing upon a variety of methodologies as well as theoretical perspectives, the symposium brings together leading experts in workplace forgiveness to: a) examine antecedents and consequences of different types of forgiveness-related responses; b) investigate mechanisms and moderators of relevance to forgiveness processes in the workplace; c) explore different types of forgiveness-related responses; and d) study the interplay among the perspectives of the different parties involved in workplace forgiveness. The symposium will include an interactive discussion aimed at highlighting key themes and future research avenues.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".