Diversity-Specific Empowering Leadership: An Alternative Approach to Reducing Sex-Based Bias and Enabling Inclusivity
Bibliographic record
Abstract
Abstract Achieving sex-based equity in organizational leadership roles has proven to be a ‘wicked’ problem with existing diversity initiatives providing minimal improvement. In this paper, we address this issue by considering a key inhibiter to women’s leadership advancement—biased perceptions of female leaders’ competence—and links to a climate for inclusion. In Study 1 (N = 236), we develop and validate a Diversity-Specific Empowering Leadership (DSEL) measure, and demonstrate its value in predicting perceptions of female leaders’ competence when compared to alternative leadership models (empowering leadership, transformational leadership, diversity-specific transformational leadership, transactional leadership, leader diversity-valuing behavior, and inclusive leadership). In Study 2 (N = 314), we introduce sex-based diversity beliefs as a moderator in the relationship between DSEL and perceptions of female leaders’ competence. In Study 3 (N = 313), we provide support for a mediated moderation model, with sex-based diversity beliefs moderating the effects of DSEL on perceptions of female leaders’ competence. In turn, this is associated with a climate for inclusion. DSEL is collaborative and developmentally focused, and our findings suggest it may attenuate sex-based biases in perceptions of leadership, especially for those who have been most resistant to change (i.e., individuals with negative sex-based diversity beliefs). Our research offers theory that can support ethical action by advancing DSEL as a promising ‘target-specific’ leadership model for creating less biased and more inclusive work environments for all.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".