Neuroleadership: Themes and limitations of an emerging interdisciplinary field
Bibliographic record
Abstract
The relationship between brain and behaviour has perplexed philosophers and scientists since the time of the ancient Greeks. Recent technological advances have allowed neuroscience to flourish, alongside growing romanticism that reductionist studies will allow us to understand complex interpersonal behaviours. Organizational cognitive neuroscience and neuroleadership are newly established interdisciplinary fields that use neuroscientific techniques to answer questions about behaviours within organizations. Neuroleadership aims to discover screening tools for good leaders, to improve leadership skills, and to identify unconscious factors affecting behaviour in hopes of improving management and leadership practices. Although proponents of neuroleadership are optimistic, if we know anything about the functions of the human brain and our interpersonal behaviours, it is that they are exquisitely complex and context dependant. Here, we briefly discuss the major themes emerging in the new field of neuroleadership and the limitations and potential consequences of applying findings from the field prematurely and with blind optimism.
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 | 0.089 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".