When explanations for layoffs are not enough: Employer's integrity as a moderator of the relationship between informational justice and retaliation
Bibliographic record
Abstract
Victims of downsizing often perceive their layoff as being unfair, which can lead to various forms of retaliation. Informational justice, defined as providing employees with adequate explanations in a timely manner, has been prescribed as a way to mitigate the retaliation tendencies associated with unfairness perceptions. Few studies, however, have examined contexts in which informational justice might be more vs. less effective in this regard. In the present research, we explored whether employees' perception of the employer's integrity moderates the relationship between informational justice and retaliation among layoff victims. Results from a field and laboratory study suggest that informational justice helps manage retaliation only when layoff victims perceived that their employer had high (vs. low) integrity prior to the layoff. In Study 2, we found that perceived sincerity mediated the impact of informational justice by integrity interaction on retaliation.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".