Bibliographic record
Abstract
What is entrepreneurship? Since the turn of the century, there has been increased global interest in entrepreneurship both by individual theorists and by institutions. This is significant because over the last quarter of a century there has been a remarkable renaissance in terms of the recognition of small firms’ “centrality as a necessary competitive instrument in the development of a modern, vibrant and progressive economy” (Beaver and Prince, 2004, p. 34). The economics literature acknowledges the central role of entrepreneurs in economic development, the creation of wealth and evolutionary change. In the United Kingdom alone, over 5.2 million businesses are operating as of 2015; of those, 99% are SMEs, accounting for 14.5 million people in employed positions (Federation of Small Business, 2015). The literature suggests that it is entrepreneurs who are the driving force of such a revolution, in the form of an economic trend that is transforming and in some cases renewing economies worldwide, contributing not only to employment but also to economic, social and political stability. Therefore, it is vital to develop an understanding of the complex field of entrepreneurship by drawing on the early entrepreneurship literature, and by evaluating and understanding the wider contributions to the now-established distinctive economic theories of the entrepreneur.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".