Words, Voice, and Body: Leaders’ Verbal and Nonverbal Communication and Their Consequences
Bibliographic record
Abstract
Scholars spanning different fields within the social sciences have long recognized that communication is a key element of leadership. While early research on leader communication focused largely on how communication revealed leader characteristics, researchers in later decades have expanded their focus to how leader communication affects followers, stakeholders, and organizations. In addition, thanks to advancing analytical technologies such as NLP- and AI-based tools, scholars can now assess leader communication at greater scale (e.g., big data) and in more diverse forms (e.g., text, vocal tone, facial expressions, body gestures). This series of papers speak to these research trends, and document several key ways in which leaders’ verbal and nonverbal communication affects consequential outcomes, from leaders’ own career outcomes to stakeholders’ reactions to that communication. The five studies take place in diverse empirical contexts and feature diverse methods in studying leaders’ communication data. The symposium will offer valuable insights into leaders’ influence processes through their communication, and showcase various ways scholars can study them. The Impact of CEO Gender on the Self-Promotion-Dismissal Relationship Author: Jungwoo Ha; UCLy - ESDES - U. of Lyon Author: Margaret Ormiston; George Washington U. Author: Elaine M. Wong; U. of California, Riverside Market Response to War Language Author: Donal Crilly; London Business School Author: Joao Cotter Salvado; Catolica Lisbon School of Business and Economics How Leaders Build Relationships in High-Stakes Conversations Author: Evita Huai-ching Liu; Bocconi U. Author: Michael Yeomans; Imperial College Business School Are Nonverbal Displays of Dominance and Prestige Likely to be Universal Signals? Author: Zak Witkower; U. of British Columbia Author: Jessica Tracy; U. of British Columbia Author: Nicholas Rule; U. of Toronto Innovating The Development of Leadership Language with Artificial Intelligence Author: George Banks; UNC Charlotte Author: Wenwen Dou; U. of North Carolina, Charlotte Author: Srijan Kumar; Georgia Institute of Technology Author: Scott Tonidandel; UNC-Charlotte
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".