Grand challenges in organizational justice, diversity and equity
Bibliographic record
Abstract
This inaugural article founding the Frontiers Journal Section on Organizational Justice, Diversity and Equity highlights four broad areas requiring further research in our field. First, organizational justice and DEI share common threads, and there is considerable room for work that conceptually integrates these two areas of study. Specifically, we need research that helps us understand how organizations as inequality-producing systems create and maintain perceptions of (un)fairness when individuals receive unequal rewards for their contributions, particularly in diverse workplaces. Furthermore, research is needed to enhance understanding of how to create and maintain high levels of organizational justice for both marginalized and predominant identity groups. Additionally, this is a space for empirical work that replicates prior findings, something that is essential to the development of science. It is also important to expand the scope of justice and DEI scholarship with a greater inclusion of research contexts from the Global South. Finally, Organizational Justice and DEI topics are inflamed in the contemporary U.S. context, and there is a need for investigation of how the societal context influences the development of our field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".