Mapping ethical, legal, & social implications (ELSI) of assisted reproductive technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: A significant portion of the research on assisted reproductive technologies explores ethical, legal, and social implications. It has an impact on social perceptions, the evolution of norms of clinical practices, regulations and public funding. This paper reviews and maps the geographical distribution to test the hypothesis of geographical concentration and classifies the output by fields and topics. METHODS: We queried PubMed, Scopus and the Web of Science for documents published between 1999 and 2019, excluding clinical trials and medical case reports. Documents were analyzed according to their titles, abstracts and keywords and were classified to assisted reproductive fields and by Topic Modeling. We analyzed geographic distribution. RESULTS: Research output increased nearly tenfold. We show a trend towards decentralization of research, although at a slower rate compared with clinical assisted reproduction research. While the U.S. and the U.K.'s share has dropped, North America and Western Europe are still responsible for more than 70%, while China and Japan had limited participation in the global discussion. Fertility preservation and surrogacy have emerged as the most researched categories, while research about genetics was less prominent. CONCLUSIONS: We call to enrich researchers' perspectives by addressing local issues in ways that are tailored to local cultural values, social and economic contexts, and differently structured healthcare systems. Researchers from wealthy centers should conduct international research, focusing on less explored regions and topics. More research on financial issues and access is required, especially regarding regions with limited public funding.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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 it