Immigrant Capital and Entrepreneurial Opportunities
Bibliographic record
Abstract
Objective: The main objective of this study is to define and operationalize the concept of immigrant capital, a key factor that differentiates immigrant from host country entrepreneurs in how they recognize and start new ventures. Research Design & Methods:Using grounded theory, we synthesized the outcomes from the analysis of eight Canadian and U.S. case studies of successful immigrant entrepreneurs with the key findings from the literature to define and develop a model of immigrant capital.Findings: Based on our grounded theory development process we show that the concept of immigrant capital as a distillate of human, cultural, economic and social capital that goes beyond expected opportunity recognition (OR) drivers like prior knowledge and prior experience to differentiate and enhance the immigrant entrepreneur's ability to recognize business opportunities compared to host country entrepreneurs.Implications & Recommendations: Understanding a unique resource like immigrant capital, will help immigrant as well as host country entrepreneurs further develop their opportunity recognition ability by bridging gaps and fulfilling the needs for both, immigrant and host country consumers. Contribution & Value Added:The main contribution is the theoretical development, identification and definition of the immigrant capital model and propositions that will articulate the factors that lead to the conceptualization and operationalization of immigrant capital.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".