Introduction: Re-positioning migrant entrepreneurs: A situated and relational approach to practices and policies
Bibliographic record
Abstract
In 2024, international migrants numbered 304 million, nearly double the 1990 figure, emphasising the urgency of understanding migrant entrepreneurship in a global context. While much scholarship acknowledges individual traits such as cultural orientation, human capital or risk tolerance, it often assumes the neutrality of the institutional and political systems migrants must navigate. This special issue challenges such individualistic framings by foregrounding migration regimes – the political and regulatory processes governing mobility, settlement and economic participation – as central to shaping entrepreneurial opportunities. Across six contributions, the issue critically examines how support systems, policies and institutional practices embed migrant entrepreneurship within structural inequalities. The articles collectively highlight how access to markets, technologies and entrepreneurial ecosystems is mediated by intersectional factors including race, gender, legal status and socio-economic background. Rather than celebrating migrant resilience or focusing narrowly on venture outcomes, the articles explore how institutional intermediaries, support programs and policy environments both enable and constrain entrepreneurial possibilities. By situating migrant entrepreneurship within broader socio-political and regulatory contexts, the special issue reorients the field away from overly individualistic narratives and toward structurally informed perspectives. In doing so, it advances theoretical coherence, highlights the lived experiences of migrant entrepreneurs and provides policy-relevant insights for designing support initiatives that recognise and address systemic inequalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".