Transdisciplinary Chair in the Context of Leadership in Organizations
Bibliographic record
Abstract
Abstract: The Transdisciplinary Chair in the Context of Leadership in Organizations is an innovative academic position that promotes the integration of multiple disciplines to address the complex challenges of leadership in the modern organizational environment. This chair seeks to transcend traditional boundaries between fields of study, encouraging collaboration among experts in corporate management, psychology, sociology, information technology, and other relevant areas to develop leaders capable of navigating and thriving in an everevolving business landscape. AI has played a critical role in improving the operational efficiency of organizations. The future will see an even deeper integration of this technology into the leadership context. AI will not replace leaders, but will serve as a strategic enabler, empowering them with predictive analytics, enabling more informed and faster decision-making. The future of leadership in organizations is shaping up at a rapid pace, in line with the evolution of emerging technologies, especially Artificial Intelligence (AI). The leadership of the future will be characterized by collaboration between humans and machines. The human skills of empathy, creativity and intuition, combined with the analytical precision and data processing capacity of AI, will create a hybrid leadership model. However, the best leaders cannot be replaced by AI, and the adversities that arise in the applicability of AI have the need to identify and improve leadership skills with a transdisciplinary approach eminent, in order to enhance and help leadership focus its cognitive energy, improve its collaborative and problem-solving skills.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".