Leadership constructs and artificial intelligence: Introducing a novel organizational assessment survey
Bibliographic record
Abstract
This theoretical paper presents a novel "Kinematic Model" of leadership, designed to capture the dynamic nature of leadership within organizations considering the environments in which they arise amidst the context of continuous change. At the core of every organization are three fundamental components: people, processes, and resources. The leadership landscape consists of 34 distinct constructs, from traditional styles such as transformational and transactional to newer ones like digital and neuroleadership. Leadership operates within varied environments, influenced by internal and external events, as well as the nature and experiences of the individuals and groups comprising organizations. The Kinematic Model integrates these elements into ten domains, emphasizing the need for continuous assessment, adaptability, and balancing people, processes, and resources. Taken together this review provides an orientation and reference to a separate comprehensive survey developed in parallel that provides a framework for any leadership assessment in various organizational settings. In the context of artificial intelligence (AI), its integration into organizations significantly affects leadership dynamics. AI enhances decision-making by analyzing vast data sets, but also risks over-reliance, potentially sidelining human judgment. AI's insights into employee performance might overlook intangible leadership qualities. Additionally, ethical concerns arise with AI in leadership, including workplace surveillance and algorithmic biases. As AI takes on more leadership roles, leaders must adapt, emphasizing vision-setting, relationship-building, and fostering innovation. Leaders must stay updated and adaptable as AI evolves, balancing its capabilities with human insight, ethics, and emotional intelligence.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".