https://docs.wixstatic.com/ugd/754172_39c7dc2d69644b42afad7f7055f3e6d3.pdf
Bibliographic record
Abstract
The digital system increases productivity and innovation in all sectors, enabling organizations to reach new markets and customers, automate and streamline business processes, and create new business models, products and services.For digital entrepreneurs to thrive in EU Member States, it is necessary for all states to develop policies to support and develop digital entrepreneurship.The actions of all EU Member States are an essential condition for the development of digital technologies and for maintaining Europe's leadership as a knowledgebased economy.The paper outlines the importance of supporting and developing digital entrepreneurship.The article highlights the key aspects of the digital entrepreneurial policies of EU countries, the factors that create conditions for the prosperity and successful operation of digital entrepreneurs.The article sets out five pillars of the digital entrepreneurial framework: digital knowledge base and the ICT market, digital business environment, taxation and the financial environment, digital competences and e-leadership, entrepreneurship.The article also defines the output dimensions of digital entrepreneurship: digital transformation, digital startups.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.854 | 0.875 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".