Inclusive Leadership in Action: Advancing Social Equity Across Marginalized Communities
Bibliographic record
Abstract
While research indicates that leaders are crucial in driving the implementation of inclusion, they also face challenges in creating and sustaining inclusive workplaces (Humberd, Clair, & Creary, 2015). This symposium addresses these challenges directly, exploring how allies and beneficiaries navigate identity-based relational tensions, how to craft sustainable workplaces when leaders are not inclusive, how leaders reconcile their own identity tensions to foster inclusivity, how leaders can better manage a health-diverse workforce, and how entrepreneurs delicately balance inclusion of marginalized employees in the workforce and the financial viability of the new venture. Overall, this symposium explores the transformative potential of inclusion in fostering systemic change across diverse organizational contexts. All five papers collectively highlight how inclusive practices, whether through allyship, leadership, or the integration of marginalized groups, can disrupt entrenched inequalities and create equitable workplaces. Creating Systemic Change in Organization: Harnessing Intersectional Friction in Allied Relationships Author: Barnini Bhattacharyya; Western University Author: Patricia Helena Hein; What if Leaders Are Not Inclusive? An Upward, Interactional Framing Approach Author: Jamie L. Gloor; University of St. Gallen Author: Huong Pham; Ingolstadt University of Applied Sciences Emerging Synergistic Identities: How Autistic Leaders Reframe Past to Model Proactive Compassion Author: Nagapriya Vaidyanathan; Drexel University Author: Liza Yasemin Barnes; Drexel University Author: Christian Resick; Drexel University Investigating Leader Illness Orientation for Employees with Chronic Health Conditions Author: Alexandra Cook; University of Amsterdam Author: Alexander Zill; Mittweida University of Applied Sciences Doing Well by Doing Good: Entrepreneurs’ Perspectives on Recruiting Neurodiverse Employees Author: Christine Nittrouer; Texas Tech University Author: Sophia Rose Thomas; Texas Tech University Author: Jared Shaw Allen; Texas Tech University Author: Emily Neubert; Texas Christian University
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".