Sex after Technology: The Rhetoric of Health Monitoring Apps and the Reversal of <i>Roe v. Wade</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it