Sex after Technology: The Rhetoric of Health Monitoring Apps and the Reversal of <i>Roe v. Wade</i>
Bibliographic record
Abstract
The convergence of artificial intelligence technologies with the growth of Christo-fascist movements in the United States presents an alarming threat to women’s health, especially considering known privacy violations by the major players—all in the shadow of the US Supreme Court’s reversal of Roe v. Wade. These violations are ethotic; that is, they betray information that has been mined algorithmically to construct “user models,” bits and pieces of which are sold or otherwise circulated without true “user” consent or cooperation. Such models are best understood as algorithmic ethopoeia, mathematized representations of individuals charted as matrices of commodified categories for commercial trafficking, but also for politicians and law enforcement. Taking inspiration from abolitionist tools for resisting intersectional racism, and incorporating data feminism, we offer six categories of design heuristics to respect and maintain ethopoeic integrity, especially in the domain of women’s health in a post-Roe technological landscape, using a fundamental rhetorical concept to serve designers, as well as critics and activists.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".