Observer reactions to interpersonal injustice: The roles of perpetrator intent and victim perception
Bibliographic record
Abstract
Summary The present research contributes to a growing literature on observer reactions to injustice experienced by others. In particular, we separated two variables that have previously been confounded in prior research, namely perpetrator intent to cause harm and victim perception of harm. We expected that injustice intent and injustice perceptions would have both unique and joint effects on observer reactions. The results of three experiments in which we manipulated perpetrator injustice intent and victim injustice perceptions supported our predictions. First, we found that observers had more negative reactions toward superiors who intended to inflict high versus low levels of interpersonal injustice toward a subordinate. Second, the injustice intent of the superior influenced observers' reactions more than did victim perceptions of injustice. Third, most novel, we found that the mere intent to cause injustice generated negative reactions in observers, even in the absence of a “true” victim—that is, when the subordinate perceptions of injustice were low. Together, our results emphasize the importance of examining observers' reactions to injustice and incorporating perpetrator intentions into the study of organizational justice. Copyright © 2012 John Wiley & Sons, Ltd.
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 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 | 0.005 | 0.024 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".