Storytelling and the campaign for abortion legalization in Argentina: the emblematic cases of Ana María Acevedo and Belén
Bibliographic record
Abstract
During the recent abortion rights campaign in Argentina that culminated in the 2020 legalization of abortion on request, the names of Ana María Acevedo and Belén were widely chanted in protests, the media, and the National Congress. These young women’s stories, which became emblems of Argentine feminist movements, centered around the denial of legal abortions and the criminalization of obstetric events, especially those of poor women in the interior of the country. This article examines the role of two emblematic cases of strategic litigation that took place during the Argentine abortion rights campaign. While the literature increasingly warns about the counter-productive effects of “negative” abortion narratives and legalistic approaches to feminist emancipatory causes, I show that these emblematic cases contributed to fostering a federalized coalition that played a key role in the mobilizations for abortion rights. Based on an analysis of archives and interviews, I argue that even as feminist movements relied on dominant gender and class norms to advocate for the implementation of lawful abortions, emblematic case building empowered local feminist movements through individual and collective transformations and solidarities, broadening discursive spaces surrounding abortion in different areas of the country.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".