Approach or Avoid? Exploring Overall Justice and the Differential Effects of Positive and Negative Emotions
Bibliographic record
Abstract
As empirical research exploring the relationship between justice and emotion has accumulated, there have been key questions that have remained unanswered and theoretical inconsistencies that have emerged. In this article, the authors address several of these gaps, including whether overall justice relates to both positive and negative emotions and whether both sets of emotions mediate the relationship between overall justice and behavioral outcomes. They also reconcile theoretical inconsistencies related to the differential effects of positive and negative emotions on behavioral outcomes (i.e., performance, withdrawal, and helping). Across two field studies (Study 1 is a cross-sectional study with multirater data, N = 136; Study 2 is a longitudinal study, N = 451), positive emotions consistently mediated the relationship between overall justice and approach-related behaviors (i.e., performance and helping), whereas negative emotions consistently mediated the relationship between overall justice and avoidance-related behaviors (i.e., withdrawal). Mixed results were found for negative emotions and approach-related behaviors (i.e., performance and helping), which indicated the importance of considering context, time, and target of the behavior. The authors discuss the theoretical implications for the asymmetric and broaden-and-build theories of emotion as well as the importance of simultaneously examining both positive and negative emotions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".