Bibliographic record
Abstract
Tech-Driven Leadership: Strategies to Maximize Business Potential with Emerging Technologies draws on research from a range of international contributors, to offer a holistic exploration of leadership in the digital age; encompassing essential skills, data-driven decision-making, ethical considerations, cybersecurity, and future trends. Its breadth and depth ensure that readers gain a thorough understanding of the multifaceted challenges and opportunities associated with technology-driven leadership. Unlike purely theoretical texts, the authors provide practical guidance rooted in real-world examples, case studies, and best practices. Readers will not only grasp theoretical concepts but also learn how to apply them effectively in their own organizational contexts, empowering them to drive tangible results and navigate complex challenges with confidence. Drawing on insights from diverse disciplines such as business, technology, ethics, and psychology, Tech-Driven Leadership offers a unique interdisciplinary perspective. By synthesizing knowledge from various fields, it provides readers with a comprehensive toolkit for addressing the multifaceted aspects of tech-driven leadership, making it a valuable resource for scholars, practitioners, and students alike.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".