Diversity Research Pushed to the Margins? Status, Stigma, and Self-group Distancing Effects
Bibliographic record
Abstract
Diversity, equity, and inclusion (“DEI”) is a field of research that, despite its 50-year history, remains at the margins of management scholarship. If the field of management is to contribute influential research on topics such as bias and discrimination, which have significant organizational and societal relevance, it is imperative to address the issue of the marginalization of DEI research, and how it may be perpetuated in the power structures of academia. We draw on status, stigma, homophily, representative bureaucracy and self-group distancing perspectives to explain and pose predictions on the factors that contribute to the marginalization of research. We examine race, gender and intersectional diversity of authors of articles on DEI topics, as well as in journal leadership teams, at 14 top-tier Management journals over a 20-year timespan, from 2001 to 2021, across five timepoints. We find that DEI research is more likely to be conducted by equity-deserving scholars. While the careers of equity-deserving scholars do not appear to be limited by their choice of research topic, this is not the case for white men who engage in DEI research. The results did not show that more diverse journal leadership influences the amount of DEI research that is published. We discuss the implications of our findings in terms of the advancement of DEI theories.
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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 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".