On a Refined Theory of Racial Diversity Congruence: An Extension to Richard et al.’s (2021) Study
Bibliographic record
Abstract
Richard et al.’s (2021) recent AMJ article aims to demonstrate that matching levels of racial diversity in upper management and lower management (i.e., racial diversity congruence) leads to optimal firm productivity. In this study, we point out several flaws in their study. For instance, it is premature to theorize a match between upper management racial diversity (UMRD) and lower management racial diversity (LMRD) as the locus of optimal firm productivity. Their empirical testing is also flawed. We then integrate the upper echelons theory and the holistic perspective of congruence to build a refined theory of racial diversity congruence. Specifically, we theorize that a slight mismatch between UMRD and LMRD with UMRD slightly exceeding (leading) LMRD leads to optimal firm productivity. We also propose the boundary condition of this congruence effect. We test and provide empirical support for our refined theory using Richard et al.’s (2021) published data. Our study not only offers a refined way to expand the upper echelons theory, but also calls for revising or abandoning many conclusions drawn in Richard et al.’s study, thereby improving the theoretical and practical implications of their study.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".