Forced reproduction: abortion access in a landscape of data violence
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.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".