Bibliographic record
Abstract
This monograph presents a comprehensive framework for international entrepreneurship (IE). To make our contribution cohesive, first, we focus our attention on definitions; then, by providing an in-depth analysis of the impacts of both internal and external factors on the decision-making processes of entrepreneurs in the realm of IE, we elaborate on the implications within this domain. Moving beyond existing literature, we use a multi-level analysis. Within this framework, we scrutinize three fundamental units of analysis: the individual entrepreneur, the firm, and the country. It is posited that this approach will facilitate a comprehensive comprehension of the considerations pertinent to international entrepreneurship, along with the principal factors at each level of analysis. By encompassing all three levels, our objective is to illuminate the interconnectedness between individual traits, firm competencies, and national circumstances that shape international entrepreneurial activities. Moreover, we adopt a behavioral perspective to scrutinize how international entrepreneurs perceive, evaluate, and capitalize on opportunities across borders. This lens enables us to acquaint our erudite audience with the decision-making procedures of these individuals. Consequently, this approach is expected to yield a more profound and nuanced insight into the motivations, risk assessments, and cognitive predispositions that shape the international entrepreneurial behavior of entrepreneurs. We believe this monograph will serve as a comprehensive and integrated resource for scholars and practitioners interested in international entrepreneurship.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".