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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".