How Justice Theory and Research Can Help Address Organizational <i>and</i> Societal Problems
Bibliographic record
Abstract
Organizational justice scholars have examined the consequences and causes of employees’ fairness perceptions. Given the reliability of what is known about how, when, and why fairness perceptions matter, we can and should contribute to addressing the pressing problems of our times, regardless of whether they primarily reside within organizations (e.g., diversity, equity, and inclusion (DEI)) or outside of organizations (e.g., climate change, political extremism). Our focus aligns with more general calls for responsible management research (Tsui, 2022). Accordingly, we illustrate the implications of organizational justice scholarship for addressing three issues: DEI, climate change, and political extremism. We also consider some of the barriers associated with translating organizational justice theory and research to practice, offer some recommendations on how to overcome those barriers, and delineate some of the unintended consequences of our best efforts. Finally, we describe ways in which organizational justice scholars can make our knowledge more accessible in public domains.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".