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Record W4294287371 · doi:10.1177/00207152221118930

Genetic identity as a regime of truth: Same sex and transnational surrogacy parenthood in the United States and Israel

2022· article· en· W4294287371 on OpenAlexvenueno aff
Daphna Birenbaum‐Carmeli, Sharmila Rudrappa

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2022
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSperm donationGrandparentNarrativeCitizenshipState (computer science)Political scienceIdentity (music)GenealogySociologyGender studiesLawBiologyGeneticsHistory

Abstract

fetched live from OpenAlex

Through examining cases of cross-border surrogacy in Israel and the United States, we offer the concept of genetic kinning defined as the narratives deployed by individuals that give prominence to genetic relatedness between offspring and parents to highlight immutable similarities between parents, and by extension, grandparents and ancestors. The deployment of genetic kinning narratives does not happen in a vacuum; instead, nation-state bodies emphasize genetic relatedness within the family unit, especially accentuated in cases of cross-border surrogacy where intended parents need to receive travel documents, including passports, and subsequently citizenship, for their children birthed through surrogacy. Genetic kinning is more emphasized for queer couples, where only one (or neither) of the fathers, or mothers as the case may be, is genetically related to the infant. We examine cases in Israel and the United States that we selected due to their wide media coverage and studied through their press presentations. We show that far from becoming less relevant, genetic relatedness becomes increasingly salient because of assisted reproductive technologies, including gamete donation and surrogacy, especially when families move across borders, presenting states bodies with the need to parse out descendance, family status/parentage, and national membership/citizenship.

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)0.000
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 score0.275
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.059
GPT teacher head0.395
Teacher spread0.335 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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