Bibliographic record
Abstract
Artificial womb technology (ectogenesis) is commonly associated with visions of science fiction societies where babies are manufactured and grown outside the woman’s body; however, ectogenesis is well on its way to becoming a reality. We reviewed literature related to the artificial womb and the discourses around this technology. Literature was collected from the following databases: ScienceDirect, Compendex, IEEE, Communication Abstracts, Scopus, OVID(All), EBSCO(All), Academic One File, Web of Science, and JSTOR. Out of 194 articles, 133 were included (based on relevance to the topic). Current literature on artificial wombs mainly focuses on feminist issues of whether or not this technology will liberate or oppress women, and in the context of the abortion debate. However, the use of artificial wombs has implications for gender relations and women’s rights on a global scale. For example, what affect will ectogenesis have on women’s autonomy, social well-being, and status in low- and middle-income countries? What will be the effect on family structure and gender imbalances that are already present in countries like India and China? We outline several future possibilities and implications of artificial womb technology. Furthermore, the ethical, legal, economic implications go beyond those impacting just women, globally. They have meaning on a national and international level, in regards to population dynamics, social structure, international competition and development, etc, and are just logical steps away from becoming reality.
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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.070 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| 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".