Documentary noir in the city of fear: Feminicide, impunity and grassroots communication in Ciudad Juarez
Bibliographic record
Abstract
The emergence of a systematic campaign of horrific violence directed at women and girls in the Mexican border city of Ciudad Juarez has become a focus of intense media and activist attention over the last two decades. While the mainstream media devoted much attention to ravaged bodies and sensational theories, state officials reacted to the crimes with a victim-blaming narrative that, activists have argued, provided a lethal accelerant to the violence. This paper explores the role of documentary film in the investigation and politicization of the murders and disappearances of women in Juarez. Along with activists and journalists, critical documentary filmmakers have been among the primary investigators of the crimes. In this paper, I argue that these grassroots media practices have been instrumental in opening spaces of communication that have been enclosed by pervasive fear and systemic insecurity. I pay specific attention to the ways in which Lourdes Portillo’s 2001 documentary Señorita Extraviada interrogates the crimes politically. Through its critical, engaged approach to the aesthetics and politics of evidence, I argue, the film poses a counter-narrative to the neoliberal state’s discourse of responsibilization and individualization in a context of systemic insecurity.
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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 | 0.004 |
| 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 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".