Exploring the influence of CEO and chief diversity officers' relational demography on organizational diversity management
Bibliographic record
Abstract
Purpose Drawing on the relational demography literature and a social identity perspective, several research propositions in which the authors postulate that demographic characteristics (e.g. gender and race) of senior leaders will influence the implementation and effectiveness of diversity management practices were presented. Specifically, the authors focus on the Chief Executive Officer/Chief Diversity Officer (CEO/CDO) dyad and explore independent and joint effects of CEO and CDO majority–minority group status on workplace diversity outcomes, outlining key identity-based and relational moderators (e.g. value threat, relational identity and leader–member exchange) of these relationships. Design/methodology/approach The literature on relational demography and leader–member exchange to develop propositions for future research was integrated. Findings This is a conceptual paper. There is no empirical data reported testing the propositions. Research limitations/implications The authors extended theory and research on relational demography by focusing on senior leaders in the organization and proposing that the influence of CEO and CDO demographic characteristics on the enactment of diversity practices may be contingent on key identity-based and relational processes. Originality/value The authors are not aware of any studies investigating how personal characteristics and relational processes relating to the CEO and CDO may influence the implementation and effectiveness of workplace diversity management practices. In a similar vein, the authors contribute to the research literatures on relational demography and social identity by extending the application of these theories to senior leaders in organizations and in relation to the work of CEOs and CDOs.
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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 | 0.009 | 0.026 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".