The impact of immigrant entrepreneurs℉ social capital related motivations
Bibliographic record
Abstract
The immigrant entrepreneurship literature indicates that immigrant entrepreneurs reap numerous benefits from their co-ethnic communities℉ social capital. These benefits, however, often come at a price because scholars note the potential for this community social capital to impose limitations on the entrepreneurs. While the literature largely focuses on the benefits of social capital, there is no research on what motivates the immigrant entrepreneurs to engage with their co-ethnic community in terms of contributing to, and utilizing, their co-ethnic communities℉ social capital, and the consequences these may have on their enterprises. Addressing this gap in the literature is important in the development of successful immigrant enterprises. Thus, based on a model posited by Portes and Sensenbrenner (1993), we suggest that immigrant entrepreneurs℉ motivations will influence their use of, and contributions to, co-ethnic community social capital, impacting, in turn, business success. We contribute to both the immigrant entrepreneurship and social capital research through exploring how entrepreneurs℉ motives, with respect to their co-ethnic communities℉ social capital, influence business success.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".