Towards Systemic Leadership Resilience: Proposing the Hybrid Artificial Intelligent Leader in Response to Economic Crises
Bibliographic record
Abstract
ABSTRACT Researchers now understand that the Great Recession stemmed from a “systemic leadership failure,” involving various entities such as the government, financial institutions, investors, homeowners, and regulators. Consequently, traditional leadership approaches of the time came under intense scrutiny, necessitating a shift in leadership perception and mentality. This paper conducts a comprehensive examination of existing literature on the primary leadership approaches prevalent before, during and after the global economic and financial crisis era (i.e., 2007–2009). The study aims to synthesize existing approaches and explore new leadership paradigms necessary to foster a more inclusive, prosperous, and responsible business environment. Furthermore, through critical analysis, this paper proposes a new type of leader capable of predicting, avoiding, and effectively overcoming potential future economic crises: the hybrid artificial intelligent (AI) leader. Considering contemporary technological advancements, the study contributes to the theory by providing a description of the proposed innovative leadership approach and justifying its effectiveness in addressing economic crises.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".