Encouraging Entrepreneurship and Economic Growth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The economy has seen unprecedented growth in the past two centuries, raising average incomes by 30-fold. With this added wealth, living standards also improved greatly. Although many factors impact economic growth, it is accepted that entrepreneurship plays a key role. Therefore, understanding the antecedents of entrepreneurship and the link to economic development, often through institutions, should be of higher importance to researchers and policymakers. This Special Issue of the Journal of Risk and Financial Management sought to provide a brief overview of the economic growth literature and its link with entrepreneurship while adding insight through the Special Issue papers regarding the drivers of entrepreneurship in different contexts. Thus, the papers gathered here addressed several aspects of entrepreneurship and how it may be encouraged through networking, cornerstone investors in initial public offerings, new financing methods such as with cryptocurrencies, and through entrepreneur health. The research sites were primarily in Asia. This lead paper summarizes the issue’s papers while also providing a short overview of the economic growth literature and its link to entrepreneurship and institutions. This Special Issue, thus contributes to the empirical and theoretic research on the drivers of entrepreneurship and the association with economic growth.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it