Characteristics of Immigrant Entrepreneurs and Their Involvement in International New Ventures
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
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.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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