The expatriate entrepreneur: Demystification and conceptualization of an international career phenomenon in the era of COVID-19
Bibliographic record
Abstract
International mobility brings new avenues for career development. Although the literature in human resources management has extensively investigated the traditional assignment cycle of expatriates by multinationals abroad, only few studies have focused on other forms of expatriation. Among these forms is the "expat-preneurship" whereby the expatriate decides to become an entrepreneur in the host country. This phenomenon is challenging career development in bringing new work dynamics. This conceptual paper presents a demystification of this growing phenomenon and provides a better understanding of this international career dynamic in the context of the new normal brought by the impacts of COVID-19 pandemic. Although many expatriates have opted to return home due to the fallout from the coronavirus pandemic, others have chosen to embrace an entrepreneurial career abroad. This paper sheds new light on this career phenomenon in which some individuals, despite pandemic uncertainty, see opportunities where others see roadblocks.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".