Language of Change: Building an Inclusive Culture Through Diversity Rhetoric and Narrative
Bibliographic record
Abstract
A fundamental culture change towards more inclusion in organizations is required to fully utilize the benefits of diversity. However, the know-how of creating a culture of inclusion has been underexplored. To address this gap, we explain how the diversity rhetoric and narratives organizations utilize play an essential role in the creation of such a culture through their impact on social majority middle managers’ identity as leaders and their inclusive leadership behaviors. Drawing from the leadership perspective of culture change and the organizational change literature, we theorize a progress model of culture creation, such that organizational diversity rhetoric and narratives can either activate or subdue threats to social majority middle managers’ leader identity, which can in turn decrease or increase their inclusive leadership behaviors. In this way, behaviors modeled by middle managers shape the culture of the team they lead, and the team-level culture change will eventually spread to affect the organization-level culture. We further propose two moderating mechanisms affecting the strength of the threat response: middle managers’ past experience managing diversity and change and their perceived fairness of the organizational diversity policies.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".