Feminist Perspectives on Rape-Revenge and Necroempowerment in Narcotelenovelas and B Movies
Bibliographic record
Abstract
Abstract This chapter offers feminist perspectives on the violence exercised by female avengers in popular audiovisual products about narcotrafficking from Mexico and Colombia. Through the case studies of the narcotelenovela Rosario Tijeras (RCN Televisión, 2010) and the B movie Sanguinarios del M1 (Alonso Ortiz Lara, 2011), we explore how recent Latin American narco-narratives rearticulate the ‘rape-revenge’ film. Following Valencia's conceptualisation of necroempowerment (2012), we argue that female characters respond to rape with ruthless methods in an effort to regain agency. We combine existing literature in feminist film studies with postcolonial readings of the specificities of rape-revenge in the narco-universe where the violence these heroines use as retaliation is already the norm for their male counterparts. A close reading of revenge sequences underscores how vengeance can constitute a cathartic outlet for enraged female characters, challenging stereotypes of feminine passiveness and subverting gender hierarchies. However, it also perpetuates a patriarchal order based on toxic ideals of individual power achieved through bloody methods. We examine how empowerment can entrap female protagonists and serve to differentiate the types of violence that each gender has access to, and we discuss the problematic representation of rape as a transformative moment necessary for women to later become powerful.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".