Characteristics of Immigrant Entrepreneurs and Their Involvement in International New Ventures
Bibliographic record
Abstract
Abstract We studied 561 young firms in Australia to understand the involvement of immigrant entrepreneurs (IEs) in international new ventures (INVs). We found that IEs are overrepresented in INVs and have many characteristics known to facilitate INV success, including more founders, university degrees, international connections, and technical capability. Our study has implications for immigration policy and economic policy and the efficient use of a nation's human capital. This research challenges a necessity‐based stereotype of immigrant entrepreneurs by identifying areas in which immigrant entrepreneurs have natural competitive advantages over native entrepreneurs (NEs). This research makes a contribution to the theory of immigrant entrepreneurship by identifying the significant role of immigrant entrepreneurs in INVs and the suitability of immigrant entrepreneurs for the development of INVs. We inform diverse streams of research in transnational and immigrant entrepreneurship with broader strategic work on the creation of INVs. © 2013 Wiley Periodicals, Inc. This research was partly funded by an Australian Academy of Social Sciences research grant. A previous version of this paper was presented at Babson College Entrepreneurship Research Conference (BCERC), 2011, Syracuse.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".