Emerging “repronubs” and “repropreneurs”: Transnational surrogacy in Ghana, Kazakhstan, and Laos
Bibliographic record
Abstract
In this article, we offer an analysis of the development of “repronubs”: less-known locations offering small-scale, niche cross-border gestational surrogacy or surrogacy services for a regional market. This analytical category of “repronubs” is useful to describe the formation of the industry from small local sites to those offering cross-border services. Based on our work in these locations, we compare the markets, regulatory contexts, and organization of the industry in Ghana, Kazakhstan, and Laos, focusing on the “repropreneurs” or surrogacy facilitation agents as pivotal in the emergence of these sites. These “repronubs” highlight the surrogacy trade between countries of the Global South and are established next to or instead of the more well-known North–South destinations. We document how surrogacy itself is increasingly stratified between higher cost and better-regulated environments such as in certain states of the United States or Canada and lower cost, less well regulated, and regionally focused environments in the settings we describe. These locations are characterized by poor or liberal regulations, the existence of local in vitro fertilization (IVF) expertise, and the emergence of local repropreneurs driving the trade using their social networks. The growth of demand from China and the growing affluent middle class in Africa is creating further markets for such regional “nubs.” Studying surrogacy in such locations is made difficult by the secrecy and confidentiality surrounding it.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".