Becoming entrepreneurs: how immigrants developed entrepreneurial identities
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the important process of how entrepreneurial identity is formed and constructed, with the perspective that entrepreneurial identity is social and dynamic, constantly shaped by various life episodes and human interactions, rather than static and unchanging. Design/methodology/approach This qualitative study comprises 30 in-depth interviews with Chinese immigrants in West Canada. These immigrants had been employed professionals under the “Skilled Workers” immigration category but later became entrepreneurs. None of the entrepreneurs in this study had prior business ownership experience, and many of them said that they had never thought about running businesses until they came to Canada. Findings A process model of entrepreneurial identity construction is presented. This paper advances the literature on entrepreneurship through the identification of three stages in the development of entrepreneurial identity: identity exploration, entrepreneurial mindsets building, and narrative development. Originality/value This study has important implications for the understanding of the exploratory and discovery mode of entrepreneurial identity construction. This study also moves away from the contextual and structural hypotheses as the sole explanations for the high rate of self-employment among immigrant entrepreneurs, and provides a useful starting point for a deeper understanding of the agency of immigrant entrepreneurs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".