Trends in engagement in surrogacy by nationality 2018–2020: a survey of surrogacy agencies
Bibliographic record
Abstract
Introduction: Global reproductive destinations are constantly changing, and so is the cross-border reproductive movement of both gametes and intended parents seeking to build families. Quantifying the use of cross-border surrogacy is difficult given no reliable data exists on the global patterns of use of CBRC generally or surrogacy in particular. Methods: Approximately 150 surrogacy agencies in countries offering surrogacy to foreigners were invited to provide consolidated data on the number of commissioning singles and couples who signed with their agency for surrogacy services over the 2018–2020 calendar years, by nationality of the biological intended parent(s). Twenty-four agencies (16%) reported on 5968 clients. Results: Other than the United States, agencies were involved predominately in arrangements for foreigners. In the United States, other than the large domestic market, France, Australia, and Israel were large source countries. Australia and France were also large source countries for Canada. China was overwhelmingly the largest source country engaging in Ukraine. In Georgia, other than Georgian nationals, China, Israel, and the United States were the largest foreign source countries. In Greece, apart from locals, the largest source countries were Italy and Australia. Colombia reported a wide spread of foreign clientele with Israel, the United States, and Canada as the largest. COVID-19 had a significant impact on enrolments in Georgia and Ukraine—each experienced large declines in enrolments when comparing 2019–2020. In contrast, despite travel bans in 2020, agencies in 3 of 4 unregulated countries continued to report increases in enrolments. Conclusions: The global cross-border surrogacy market remains highly volatile, experiencing rapid growth and decline, especially in newer destinations. Source countries providing surrogacy clients are based partly on proximity and cultural ties, but more so on affordability. Further research is required to monitor the effects of engagement in newer destinations with fewer protections.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".