New Required Characteristics for Entrepreneurs Trans-COVID-19 Vis-a-Vis Pre-COVID-19
Bibliographic record
Abstract
This qualitative systematic study analyzes the entrepreneur concept and its transformation during the COVID-19 pandemic. The study reports the results of a metasearch of keywords such as entrepreneurial traits, entrepreneur’s characteristics, business adaptations due to COVID-19, COVID-19 protocols, management responses to COVID-19, and entrepreneurship during COVID-19, among others. It has fulfilled the objective of confirming the presence of an adaptation and transformation process of the skill set and traits of a successful entrepreneur Trans-COVID-19 vis-a-vis Pre-COVID-19. Prevalent characteristics during the COVID-19 pandemic include adaptability, technological knowledge, creativity, innovation, ability to network, and creating new relationships. These characteristics suggest avenues for further investigation. Research opportunity lies in examining potential shifts in strategic orientations that may arise from the post-traumatic effects on organizations due to the profound changes brought about by the COVID-19 pandemic. Furthermore, the recognition of these new traits and shifts in managerial paradigms during the Trans-COVID-19 period raises significant questions about potential changes in academic processes aimed at developing future entrepreneurs.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".