Exploratory Study of Polyvagal Theory and Underlying Stress and Trauma That Influence Major Leadership Approaches
Bibliographic record
Abstract
Leadership approaches have evolved to incorporate rational and non-rational processes. Traditional leadership research focused on internal and external organizational influences, but this paper underscores the need for adopting modern-day approaches for investigating leadership outcomes. Neuroscience can illuminate different cognitive effects that influence leadership. The research paper highlights the importance of attitudes towards leadership due to the complexity of modern organizational influences. The main forces highlighted are polyvagal theory, underlying stress, and trauma. A literature review provides a description of the fundamental neural and cognitive drivers of leadership. The paper also explains the findings of research studies demonstrating the correlation between neurocognitive processes and three leadership approaches: transactional, transformational and servant leadership. The discussion section elaborates these findings to determine whether insights can be applied in typical organizational settings. Lastly, the conclusion section summarizes the main deductions and explains limitations and recommendations for future exploratory investigations on rational and non-rational leadership choices. Overall, the paper attempts to justify why non-rational drivers carry equal weight as the rational influences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".