Co-Designing Reproductive Justice Futures: Intersectional Care-Activism for Abortion Access
Bibliographic record
Abstract
This dissertation project will integrate Feminist HCI and Reproductive Justice to interrogate how current technologies being used to address gaps in abortion access risk perpetuating reproductive oppression, and co-design supports for inclusive abortion access. My research looks at how the current "crisis of care" in abortion access resulting from the 2022 Dobbs decision is responsibilizing technologies and putting unsustainable demand on Abortion Access Organizations while not addressing underlying systemic barriers to abortion access which were in effect prior to 2022. My project focuses on Illinois, USA, where many abortion seekers are traveling to access their care. I use qualitative methods and design as research to explore how AAOs are currently using technologies, how extant abortion technologies are leaving out particular user groups, and how design can work towards Reproductive Justice futures.
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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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.046 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".