Contextualization and conceptualization of the determinants of self-initiated expatriates’ international opportunity recognition in an informal economy context
Bibliographic record
Abstract
This study introduces an updated conceptual model that extends the contextual literature on self-initiated expatriates (SIEs) into the opportunity recognition framework. Integrating individual and contextual factors, this model examines opportunity recognition by SIEs in informal economies, an under-researched area. The current global migration context, characterized by economic opportunities, geopolitical conflicts, and environmental changes, is leading to significant demographic and labor transformations. These changes are having a substantial impact on international entrepreneurship. The new model extends experiential learning and self-construal theories, incorporating work experience in informal economies, cultural intelligence, individualism, and risk aversion. This model illustrates how SIEs leverage their diaspora connections and advanced cross-cultural competencies to navigate and utilize the entrepreneurial ecosystem effectively. Additionally, the study provides new and testable research propositions. This approach offers a comprehensive understanding of how SIEs recognize and pursue opportunities in informal economies, highlighting the intricate interplay between individual attributes and contextual influences.
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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 | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 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".