The importance of policy in perceptions of organizational justice
Bibliographic record
Abstract
Organizations create policies in an effort to reduce injustice, as well as address the needs and interests of organizational members. We argue that individuals can make fairness judgments related to organizational policies, which are independent from other dimensions of fairness (i.e. distributive, procedural, interpersonal, and informational justice). Results of a field study with 164 union members found that (a) individuals make judgments about the fairness of policies that are distinct from other forms of justice, (b) perceptions of policy justice predict variance in behaviors beyond other forms of justice, and (c) perceptions of policy justice interact with distributive and procedural justice to predict behaviors. More specifically, results show that policy justice interacts with distributive justice to predict turnover intentions and citizenship behaviors towards the union. Policy justice also interacts with procedural justice to predict turnover intentions. However, this interaction was in the opposite direction from what we originally predicted. We discuss the implications of these findings for justice research and practice, as well as provide avenues for future research.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".