Impact of embeddedness in ethnic networks on business growth : evidence from Chinese immigrant entrepreneurs in Australia
Bibliographic record
Abstract
Immigrant entrepreneurship, or, self-employment by immigrants (Light & Bonacich, 1988), has been of growing interest to researchers (Hosler, 1996). This is due in part to major immigrant receiving countries, such as Australia, the United States, Canada, the United Kingdom and Western Europe, experiencing a high growth rate in their immigrant populations, leading to a more visible presence of immigrant business in major cities (Woon, 2008). By starting their own businesses, immigrant entrepreneurs may circumvent some of the barriers and disadvantages encountered in looking for a job (Sequeira & Rasheed, 2006). Successful immigrant entrepreneurs will integrate into the economy by creating jobs, providing products and services for members of their own ethnic community and society, as well as introducing new products and services that expand consumers’ choices (Rath & Kloosterman, 2000). Immigrant entrepreneurs tend to start business within their ethnic enclave, as it is an integral part of their social and cultural context and the location where ethnic resources reside (Logan et al., 2002). An ethnic enclave is an interdependent network of social and business relationships that are geographically concentrated with its co-ethnic people (Portes & Bach, 1985).
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".