Pieces of Myself all Over the World: Multiple Identities and Diaspora Entrepreneurship
Bibliographic record
Abstract
How much is the location of one’s firm determined by the location of one’s self? As an international entrepreneur, the location of self is complicated and contingent. Global transitions are often represented in one’s self-concept, bifurcating people’s self-definitions into home and host, blood and soil, overlapping or conflicting with gender, familial and other identities.However, extant literature largely emphasizes the role of diaspora members’ “psychological bond with the country of origin” and ignores other identities that may influence diaspora economic engagement (Chand & Tung, 2014). Furthermore, the literature seems to focus on first-generation immigrants (Gillespie et al. 1999), and implicitly assumes that second generation family members identify with their parents’ country of origin, too (Alba & Waters, 2012).In reality, second generation diaspora members’ country of origin is the parents’ host country, and socialization provides a background for multiple identities (Ramarajan, 2014). The effect of multiple identity formation further increases when diaspora members are born to or part of mixed couples and/ or have a history of migration patterns across multiple countries (e.g. Indians born in Canada to Indian parents who were born in Tanzania whose grandparents were the original migrants from India). Thus, concepts such as “country of origin” become blurred. That is, operating at the intersection of multiple worlds can make working across borders desirable and potentially also more rewarding. Thus, how second generation diaspora members choose where and how to engage economically with their “home” and “host” countries is an important question. Indeed, from a long-term perspective, countries that have large, economically vibrant diasporas abroad should be interested in maintaining links and remaining attractive not just to migrants, but also their descendants.In this research, we focus on second-generation immigrants and their economic engagement with their “country of origin” and country of residence. We combine theories on multiple identities (Ramarajan, 2014) with that of diaspora entrepreneurship and investment (e.g. Sonderegger and Täube, 2010; Vaaler, 2011). Specifically, we consider how second-generation immigrants’ identities (home, host, gender and familial) influence the formation of new ventures and contrast that with other forms of economic engagement such as remittances and international career moves.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".