Virtual Academic Entrepreneurship and Virtual Transformation: The Role of Soft Skills
Bibliographic record
Abstract
Abstract This chapter explores the foundations of soft skills (SS) within the realm of virtual academic entrepreneurship, heavily influenced by the ongoing process of virtual transformation. An extensive review of the existing literature highlights that owing to the adaptable, combinable, programmable and generative nature of virtual technology, a fundamental aspect of virtual transformation, the cultivation of SS among higher education students is paramount. Given the intrinsic difficulty in assessing SS, the central research question addressed in this chapter is: What are the core building blocks of virtual academic entrepreneurship, and how do they take shape? The findings suggest that these competencies can be nurtured within three primary domains: (1) knowledge sharing, (2) cultural attributes and (3) individual attributes. Implications of this research underscore the role of virtual tools in supporting SS development, acknowledging their inherent complexity. The primary objective is to foster an entrepreneurial mindset among students, promoting the growth of virtual academic entrepreneurship based on these foundational principles. This chapter introduces an innovative conceptual framework that elucidates the dynamics of virtual academic entrepreneurship, specifically focusing on the role of SS. The framework delves into the complex relationships among these skills, virtual transformation and the disruptive influence of virtual technology. Its overarching goal is to cultivate an entrepreneurial mindset among students in higher education.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".