Making Sense of Violence Through Women’s Experiences: Meaning-Making, Gendering and Racialization at Peru’s Urban Margins
Bibliographic record
Abstract
Abstract This article examines how cultural frameworks shape interpretations of women’s experiences of violence. Based on 16 months of ethnographic fieldwork in Puerto Nuevo, a Peruvian shantytown, the study explores how residents, communities, and state actors ascribe meaning to violence through shared cultural logics of gendering and racialization. Two interwoven cultural processes emerge: gendering, which reinforces societal norms of masculinity and femininity, and racialization, which constructs racial categories and stereotypes. Participants’ interpretations of violence are deeply rooted in normative conceptions of gender and race, often blaming women for the violence they and their communities endure while framing poor, racialized residents as inherently violent. By analyzing the cultural dimensions of violence through women’s experiences, this study pushes criminological literature beyond individual or group-based (e.g. offenders, victims) analysis to examine the broader social structures shaping societal understandings of violence. Bridging cultural sociology and criminology, the study reveals how gendered and racialized meanings of violence extend beyond specific groups and contexts, reinforcing structural inequalities. Recognizing these processes is crucial for addressing systemic violence and its disproportionate impact on marginalized women.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".