Deepening Our Understanding of Inclusive Leadership: A Multi-Level, Multi-Identity-Group Perspective
Bibliographic record
Abstract
Contemporary organizations have been calling for inclusive leadership to promote workplace inclusion. However, the existing literature on inclusive leadership has not yet adequately addressed questions such as what inclusive leadership encompasses, how it influences marginalized groups, and the mechanisms through which it impacts individual and organizational outcomes. This symposium brings together four papers that collectively add novel insights into these questions, from a multilevel and multi-identity-group perspective. The four papers offer four different approaches of capturing inclusive leadership, at the individual, group, and organizational levels. They also delve into the impact of inclusive leadership on group members and particularly on marginalized groups such as women and people with disabilities. They further ask questions related to conduits of inclusive leadership at individual and organizational levels, examining new mechanisms such as disability identity threat and organizational climate of gender stereotypes. This symposium challenges the participants to re-think how inclusive leadership can be understood in novel ways, how diversity can be better integrated into this research, and how inclusive leadership impacts individuals’ perceptions, decisions, effectiveness, and organizational climate and knowledge use. Insights into these questions can broaden perspectives for future research and provide practical guidance on how to develop inclusive managers and organizations. Impact of Leader Inclusion Behavior on Employee Psychological Safety, Performance and Burnout Author: Lynn Shore; Colorado State U. Author: Beth G. Chung; San Diego State U. Author: Justin Wiegand; San Diego State U., Fowler College of Business A New Approach to Measuring Inclusive Leadership Author: Wei Zheng; Stevens Institute of Technology Author: Haoying Xu; Stevens Institute of Technology Author: Martin Osei; Stevens Institute of Technology Author: Peter G Dominick; Stevens Institute of Technology Inclusive Leadership and Disability Disclosure Author: Zoe Troxell Whitman; Columbia U. Teacher's College Author: Elissa Perry; Teachers College, Columbia U. Managing Gender Issues to Mitigate Shared Climates of Gender Stereotypes Author: Yixuan Li; U. of Florida Author: Pang Xingyu; School of Economics & Management, Tongji U. Author: Haiyang Liu; Nanyang Business School, Nanyang Technological U., Singapore Author: Zhefan Huang; U. of Florida Author: Klodiana Lanaj; U. of Florida Author: Yueting Ji; Central U. of Finance and Economics Author: Shengming Liu; Fudan U.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.011 |
| 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".