Wounded Healers: Abortion and the Affective Practices of Pro-Life Health Care
Bibliographic record
Abstract
For some post-Roe abortion providers, the emotional cost of their abortion practice was untenable. By the 1980s, former abortion providers had become prominent anti-abortion advocates. Although physicians such as Beverly McMillan grounded their pro-life conversions in medical technologies and "fetological" research, affective connections to the fetus animated their activism. McMillan explained that through abortion practice, the medical profession - her vocation - had gone astray, and her pro-life activism was the cure to the resulting emotional damage. For these physicians, emotional well-being could only be recovered through principled attempts to right the perceived wrongs of the medical profession. Another group of emotionally-engaged pro-life health workers emerged from their pasts as abortion patients. Myriad post-abortion narratives followed the same trajectory: the woman reluctantly underwent an abortion, and was subsequently plagued by apathy, depression, grief, guilt, and substance-use disorders. Pro-life research came to understand this cluster of symptoms as Post-abortion Syndrome (PAS). Some women, such as Susan Stanford-Rue, opted to heal from their pain by becoming PAS counselors. Just as the "reformed" physicians combined their affective experiences with their medical expertise to argue against abortion, the counselors merged emotion and psychiatric language to redefine what it meant to be an "aborted woman" and therefore a PAS counselor. Examining pro-life publications, Christian counseling manuals, and activist speeches, this article argues that, for these activists, science and technology provided the rationale to make abortion unthinkable, but it was the activists' emotional framework that made this rationale pro-life in the first place.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".