How Stigmatized Categories Emerge: The Abortion Clinic in the United States after Roe v. Wade
Bibliographic record
Abstract
A growing area of interest within the literature on categorization concerns stigmatized categories, i.e. groups of organizations that are collectively perceived as engaging in controversial practices. Because such practices draw high levels of societal disapproval, which in turn makes the category unattractive to potential new entrants, the processes through which stigmatized categories of organizations form represent a key empirical puzzle. In this paper, we examine stigmatized category formation through an in-depth historical case study of the emergence of free- standing abortion clinics in the United States. Through our historical analysis, we identify three mechanisms that contributed to the establishment and population of this stigmatized category: a) the founding of de novo, specialist organizations, by pro-choice advocates; b) the entry by de alio organizations championing allied causes; and c) the exit of generalist organizations, or hospitals, from abortion provision. Furthermore, we find that the above dynamics were catalyzed by social movement action both in support of and in opposition to the stigmatized practice, which exacerbated category contrast and consolidated the role of free-standing clinics as the main providers of abortion services in the U.S. We conclude by discussing the relevance of our findings for the literatures on stigma, categories, and social movements.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| 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".