A Path Forward to #NiUnaMenos Based on an Intersectional Analysis of Laws Criminalizing Femicide/Feminicide in Latin America
Bibliographic record
Abstract
Since 2007, eighteen Latin American countries have enacted laws that criminalize femicide/feminicide in an effort to address gender-based murders in the region and to uphold their obligations under international human rights law. However, the COVID-19 pandemic and its systemic lingering effects exacerbated the existent dangerous levels of gender-based violence in the region, resulting in an increase in gender-based murders. To address these murders, between 2020 and 2021, a quarter of the eighteen Latin American countries that criminalized femicide/feminicide have implemented or are in the process of implementing reforms to their laws criminalizing femicide/feminicide. Given this new trend to address the prevalence of gender-based murders, this Article analyzes the laws of nine Latin American countries from an intersectional gender lens perspective. The Article ultimately questions whether criminal law is an effective tool to prevent and eradicate feminicide, as well as to provide comprehensive reparations for survivors with multiple marginalized identities and their families for the multi-sided violence they have been forced to endure due to patriarchal, racist, and colonial-capitalist systems of power. With a vision that women will be able to live their lives free from violence, this Article describes how the path forward requires a new approach grounded in the lived experiences of those that are disproportionality impacted by gender-based violence; and provides clear recommendations for States to ensure that they recognize the murders of women with multiple marginalized identities in order to protect their right to life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".