Taking the “High Road”: Theoretical and Empirical Advances on Kindhearted Reactions to Wrongdoing
Bibliographic record
Abstract
Employees engaging in wrongs is an unfortunate workplace reality that can prove costly for organizations. Although research has shown that individuals respond destructively to such behavior, emerging work has shown that some turn the other cheek and react in a kindhearted manner (e.g., forgive, reconcile). The literature is still in its infancy; we do not have a full understanding of when and why people react in a kindhearted manner, and what the consequences are of these kindhearted reactions to organizations. This symposium addresses this research agenda by: (1) examining different antecedents of kindhearted reactions, such as features of the wrongdoing (e.g., seriousness, intentionality), individual factors of individuals engaging in the kindhearted reaction (e.g., perspective taking, narcissism), situational factors (e.g., climate, power), and different types of wrongdoing (e.g., abusive supervision, misconduct) that influence kindhearted reactions; (2) identifying different types of kindhearted reactions (e.g., coworker protective behavior, forgiveness, leniency); (3) highlighting behavioral strategies and psychological mechanisms (e.g., gossip, unfairness) that explain how kindhearted reactions influence outcomes; (4) exploring distal consequences of kindhearted reactions, such as emotions, perceptions, and behaviors; and (5) uncovering moderators (i.e., offender need, gender, mindfulness) that influence the impact of kindhearted reactions on downstream outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".