Insights from migrant entrepreneurship in Portugal pre- and post- COVID- 19
Bibliographic record
Abstract
This study explores the resilience of Brazilian businesses in Porto, Portugal, focusing on those that survivedthe COVID-19 pandemic. Entrepreneurs were interviewed before and after the pandemic, providing insightsinto the local entrepreneurial ecosystem (EE) from a migrant perspective. Using a mixed embeddednessapproach, the research examines the pandemic’s impact on these businesses and identifies attributes criticalto their survival. This study highlights conceptual gaps in the EE framework, particularly regarding small businessesand migrant entrepreneurship during crises. Through Eisenhardt’s case study methodology, data werecollected via 32 in-depth interviews before the pandemic, with follow-ups revealing that only 15 businessesremained operational. Five surviving entrepreneurs were interviewed in detail. Resilient businesses, suchas restaurants and computer/cell-phone repair shops, adapted by offering in-home services and leveragingsocial media to engage clients, demonstrating creativity and risk-taking. Unlike many local entrepreneurs,Brazilians exhibited higher risk tolerance, using government financial aid not only for personal needs butalso to sustain their businesses. This research contributes to the literature by addressing theoretical gaps inmixed embeddedness and EE concepts, while applying an innovative analytical framework combining mixedembeddedness, resilience strategies, and resource orchestration theory. It offers practical implications forpolicymakers and stakeholders, emphasizing the economic and social integration of migrants. Tailored supportstrategies are recommended to address the unique challenges migrants face, underscoring the broadercontributions of migrant businesses to economic growth and social cohesion in host communities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| 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".