Implications of Collective Biological Processes for Teams and Organizations
Bibliographic record
Abstract
This symposium will explore how biological mechanisms such as neural synchrony, emotional arousal, and physiological alignment shape team dynamics, leadership effectiveness, and workplace interactions. Featuring cutting-edge research, the session will examine shared mental models, leader-follower alignment, third-party reactions to abuse, and the role of charismatic leadership in fostering creativity. By integrating biological and organizational perspectives, this symposium provides actionable insights into the mechanisms driving collaboration, decision-making, and innovation in teams and organizations. Stand Up or Stand Down: Neural and Emotional Dynamics in Third-Parties to Abusive Supervision Author: Nguyen Chan Pham; Rutgers Business School Author: Chao Chen; Rutgers University On the Same Page and Wavelength? Investigating Shared Mental Models and Inter-Brain Synchrony Author: Imogen Weigall; University of South Australia Author: Ruchi Sinha; Nanyang Business School Author: Ina Bornkessel-Schlesewsky; University of South Australia Author: Matthias Schlesewsky; University of South Australia Author: Zachariah Cross; Leader Value Signaling versus Follower Supplementary Fit: Untangling the Effects on Group Synchrony Author: Shuai Ren; McMaster University Author: Yair Berson; McMaster University Author: Rick D. Hackett; McMaster University Harnessing Synchrony: The Role of Charismatic Leadership in Fostering Creativity Author: Alon Burns; Author: Yair Berson; McMaster University Author: Ilanit Gordon; -
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".