How Justice Theory and Research Can Help Address Organizational <i>and</i> Societal Problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it