Genetic identity as a regime of truth: Same sex and transnational surrogacy parenthood in the United States and Israel
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".