An overview of contemporary ethnic entrepreneurship studies: themes and relationships
Bibliographic record
Abstract
Purpose The aim of this study is to explore the status of contemporary ethnic entrepreneurship studies in 1999‐2008 in order to map the intellectual structure of ethnic entrepreneurship research and to provide insights for future research in this field. Design/methodology/approach This study collected citation data from SSCI, resulting in a data set of 403 journal articles and 18,656 cited references. Then using co‐citation analysis, this study identified the core research themes in the ethnic entrepreneurship literature in 1999‐2008. Findings The results showed that contemporary ethnic entrepreneurship studies clustered around a few key research themes and their research foci have shifted from research on enclave economies, ethnic enterprises, and social embeddedness to research on immigrant entrepreneurs, immigrant networks, and transnational entrepreneurs. Research limitations/implications With the qualification of citation and co‐citation analysis, this study profiles the changing paradigms of contemporary ethnic entrepreneurship studies and traces the development of ethnic entrepreneurship research, and thus provides important insights on future ethnic entrepreneurship research, including transnational entrepreneurs, theory refinement and theory development on ethnic entrepreneurship, as well as ethnic culture and entrepreneurship. Limitations of using SSCI data are also discussed. Originality/value The intellectual structure of ethnic entrepreneurship literature has received relatively little attention in spite that a large number of studies have been done in this field. This study provides researchers with a new way of profiling key themes and their relationships in ethnic entrepreneurship, which will help the academia and practitioners better understand contemporary ethnic entrepreneurship studies.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.029 | 0.054 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".