Reimagining disability: the screening of donor gametes and embryos in IVF
Bibliographic record
Abstract
In this article, we examine how disability is figured in the imaginaries that are given shape by the reproductive projects and parental desires facilitated by the bio-medical techniques and practices of assisted reproductive technologies (ARTs) that involve selection and screening for disability. We investigate how some users of ARTs understand and deploy these imaginaries in ways that are both concordant with and resistant to the understanding of disability embedded within the broader sociotechnical and social imaginaries. It is through users' deliberations, choices, responses, and expectations that we come to understand how these imaginaries are perpetuated and resisted, and how maintaining them is also dependent upon the individual actions and actors who have internalized them. Our examination is grounded in a close analysis of a small selection of interviews drawn from data gathered during a 4-year project funded by the Australian Research Council exploring the Australian experience of cross border reproductive treatment, looking particularly at surrogacy, and gamete and embryo donation. Our interviewees were individuals or couples who used gamete or embryo donation, coupled at times with surrogacy in attempting to have a child. Participants discussed their views on testing, screening, and future disability.
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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".