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Record W4406302216 · doi:10.1080/14616742.2024.2439336

Storytelling and the campaign for abortion legalization in Argentina: the emblematic cases of Ana María Acevedo and Belén

2025· article· en· W4406302216 on OpenAlexafffund
Rose Chabot

Bibliographic record

VenueInternational Feminist Journal of Politics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLatin American and Latino Studies
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaMcGill University
KeywordsLegalizationPolitical scienceStorytellingAbortionLatin AmericansGender studiesSociologyArtNarrativeLawLiterature

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.171

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.350
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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