The Myth of the Gendered Chromosome: Sex Selection and the Social Interest
Bibliographic record
Abstract
Sex selection technologies have become increasingly prevalent and accessible. We can find them advertised widely across the Internet and discussed in the popular media—an entry for “sex selection services” on Google generated 859,000 sites in April 2004. The available services fall into three main types: (1) preconception sperm sorting followed either by intrauterine insemination of selected sperm (IUI) or by in vitro fertilization (IVF); (2) preimplantation genetic diagnosis (PGD), by which embryos created by IVF are tested and only those of the desired sex are transferred to the woman's uterus; and (3) prenatal testing of fetuses through ultrasound or chromosomal analysis, followed by selective abortion of fetuses detected to be of the undesired sex.Victoria Seavilleklein's research was supported by the following grants: Izaak Walton Killam Memorial Scholarship, Social Sciences and Humanities Council Doctoral Fellowship, and CIHR Training Program in Ethics of Health Research and Policy. Earlier versions of this paper were read to the Philosophy Department at Dalhousie University and to the participants of the CIHR Training Program in Ethics of Health Research and Policy. We are grateful for the helpful feedback we received on both occasions. We also appreciate the comments made by Micah Hester and two anonymous reviewers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".