Authentic Mothers, Authentic Daughters and Sons: Ultrasound Imaging and the Construction of Fetal Sex and Gender
Bibliographic record
Abstract
Abstract: At a time when many women choose fewer medical interventions at birth and claim less sex preference before birth, why do so many choose to use technology to determine the sex of their babies weeks before they are born? The popularity of accessing ultrasound imaging to ‘‘determine’’ fetal sex resonates with a contemporary gender liberalism that affirms binary sex, gender equality, and (limited) gender variation within the logic of consumer choice. Prenatal ultrasound technology is a central and current means of disciplining women into a normative maternal identity and authenticating the sex and gender identities of both mother and child. The ultrasound exam naturalizes the binary sex system upon which maternal and filial identities hinge, through the technological construction of the image of a normatively sexed body. By insisting upon the primacy of naturally sexed bodies even while deflecting attention from bodies onto images, commodities, and discursive behaviours, the popular discourse surrounding ultrasound imaging of fetal sex and the sharing of images of fetal genitals uniquely authenticates the binary sex system as the ‘‘biological’’ limit of gender and sexual variation. Pregnant women are not erased by the hypervisibility of their fetuses. Rather, as they correctly access the medical technologies and goods of middle-class motherhood, they become visible as the properly gendered mothers of properly gendered children.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".