Transnational Networking and Business Success: Ethnic entrepreneurs in Canada
Bibliographic record
Abstract
It is agreed that transnational networking plays an important role in the effectiveness of ethnic entrepreneurial firms. Yet, distinctions between the different types of transnational networking and their effects on business effectiveness have received scant attention in the literature, probably because ethnicity has been considered the main actor in the networkingeffectiveness relationship. This paper argues that one of the reasons business effectiveness differs across ethnic entrepreneurial firms is that ethnic entrepreneurs engage in dissimilar types of transnational networking. Analyses of the data generated by 720 ethnic entrepreneurs in Canada, revealed that ethnicity, human capital and push-pull factors play a central role in the engagement of different types of transitional networking; and the different types of transnational networking affect the business turnover (sales) and the business survival (age). Push-pull factors were found to play a marginal role in the business effectiveness. These results highlight the competitive market immigrants and members of ethnic minority groups encounter in the hosting economy and stress the value of transnational networking.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".