Measuring reproductive tourism through an analysis of Indian ART clinic Websites
Bibliographic record
Abstract
OBJECTIVES: India is fast becoming the most prominent player in the global industry of reproductive tourism, in which infertile people cross international borders to seek assisted reproduction technologies. This study was conducted to better understand the extent and manner in which Indian clinics seek foreign clients. METHODS: A systematic search of official Indian assisted reproduction technologies clinic Websites was undertaken, and instances noted where foreign clients were overtly targeted, and where maternal surrogacy was overtly offered. RESULTS: A total of 159 clinics with Web addresses were identified, though only 78 had functioning Websites. All were published in English, with the majority clustered in the states of Maharashtra (14) and Gujarat (9). Of the 78 functioning Websites, 53 (68%) featured some mention of maternal surrogacy services, and 42 (54%) made overt overtures to foreign clients. Qualitative appeals to foreigners included instructions for international adoption, visa application, and the legal parental disposition of the surrogate. All Maharashtran clinic Websites that mentioned surrogacy also overtly featured reproductive tourism. Preimplantation diagnosis services were not offered disproportionately by clinics mentioning reproductive tourism. CONCLUSIONS: Based upon clinic online profiles, reproductive tourism comprises a substantial fraction of India's assisted reproduction technologies clinics' business focus, clustering around its most tourist-friendly locales, and surrogacy may be a strong motivator for international clientele.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".