Forced reproduction: abortion access in a landscape of data violence
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 U.S. Supreme Court’s ruling in Dobbs v. Jackson Women’s Health Organization overturned federally sanctioned legal protections for abortions in America, returning legislative power over reproductive rights to individual states. Consequently, access to reproductive care has become increasingly location-dependent. The Dobbs decision and its ongoing consequences occur during a time of intensified location-data-tracking, with geofencing, licence plate reading, and IP address tracking becoming commonplace. Data intermediaries collect locational data about reproductive healthcare and their users and sell this information to courts and civil litigants, such as anti-abortion organizations. Reading this context through Anna Lauren Hoffmann’s framework of data’s discursive violence, this article is a theoretical intervention in the reproductive imperative of data intermediaries. While popular responses to tracking practices call for increased data protections, this paper challenges the position that legislation can help undo these harms. In examining the practices of data intermediaries and the legal contexts that protect them, this article argues that data intermediaries are not simply bad actors profiting from post-Dobbs regulations but they reify a culture of forced reproduction through the instrument of data violence.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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