General presentation
Bibliographic record
Abstract
It is common knowledge now that entrepreneurship plays an important role in any society because it generates economic benefits that are often larger than the private benefits reaped by the entrepreneurs themselves (van Praag, Versloot, 2007). Entrepreneurship creates jobs, enhances workforce dynamics, increases utility, and fosters productivity and socio-economic development (Ghio et al., 2015). In studying this phenomenon, researchers find strong connections between creativity and entrepreneurship (Gilad, 1984). Recently, the United Nations acknowledged the emergence of a new paradigm in which creativity and innovation are the main drivers of the world economy (UNCTAD, 2010; Araya, Peters, 2010). More and more, firms have recognized that creativity plays an important role in the making or reshaping of successful business models. Creativity enables organizations to build competitive advantages and achieve successful performance (Amabile, 1996; Anderson et al., 2014). It allows organisations to take advantage of opportunities resulting from changing environmental conditions (Shalley et al., 2004). It can provide the basis for innovation and business growth, as well as impacting positively on society generally (Bilton, 2007; Burger-Helmchen, 2012). And the list goes on. It is not surprising that creativity in entrepreneurship has been discussed extensively in the related literature. Since future advancement of the field depends on both existing scholarship and new knowledge generated by contemporary contributors, it is necessary to take stock of the contributions to date. This paper reviews the literature related to development of the literature on creativity in entrepreneurship field to suggest key directions for future research.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.848 | 0.736 |
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".