Beyond one-size-fits-all: Tailoring diversity approaches to the representation of social groups.
Bibliographic record
Abstract
When and why do organizational diversity approaches that highlight the importance of social group differences (vs. equality) help stigmatized groups succeed? We theorize that social group members' numerical representation in an organization, compared with the majority group, influences concerns about their distinctiveness, and consequently, whether diversity approaches are effective. We combine laboratory and field methods to evaluate this theory in a professional setting, in which White women are moderately represented and Black individuals are represented in very small numbers. We expect that focusing on differences (vs. equality) will lead to greater performance and persistence among White women, yet less among Black individuals. First, we demonstrate that Black individuals report greater representation-based concerns than White women (Study 1). Next, we observe that tailoring diversity approaches to these concerns yields greater performance and persistence (Studies 2 and 3). We then manipulate social groups' perceived representation and find that highlighting differences (vs. equality) is more effective when groups' representation is moderate, but less effective when groups' representation is very low (Study 4). Finally, we content-code the diversity statements of 151 major U.S. law firms and find that firms that emphasize differences have lower attrition rates among White women, whereas firms that emphasize equality have lower attrition rates among racial minorities (Study 5). (PsycINFO Database Record
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".