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Record W3195678169 · doi:10.1093/jlb/lsaa067

Reimagining disability: the screening of donor gametes and embryos in IVF

2020· article· en· W3195678169 on OpenAlexaff
Isabel Karpin, Roxanne Mykitiuk

Bibliographic record

VenueJournal of Law and the Biosciences · 2020
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsYork University
FundersDivision of Arctic SciencesUniversity of Technology SydneyLondon School of Economics and Political Science
KeywordsEmbryo donationDonationSelection (genetic algorithm)Reproductive technologyEgg donationThe artsSociologyPolitical scienceGender studiesPublic relationsMedicineEmbryoBiologyGynecologyLawGenetics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.314
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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