Registering the gang body in pain: violence and haunting in Central America
Bibliographic record
Abstract
This article examines the challenges of registering the wounds carried by racialised subjects involved in extra-legal violence. It argues that reckoning with these lingering traumas demands a nuanced conception of violence that foregrounds mutual vulnerability. In Central America, gang members are routinely cast as perpetrators through colonial metaphors of animality and savagery, especially in mass media and official discourses, reinforcing the logics of the US-Central American war on gangs. As a result, the suffering endured by gang members remains largely illegible; they are recognized as agents of violence but not as its victims. Deemed invulnerable, the state, structural, and imperial harms they endure fail to register as violations. How, then, might one acknowledge these individuals as wounded subjects, haunted by the toll of racialised violence, without erasing the injuries they inflict? Such an acknowledgement is crucial for imagining an alternative ethical horizon that disrupts the punitive ideologies dominating current responses to gangs and other extra-state actors. Drawing on fieldwork with gangs in Central America and Mexico (2016–2019) and focusing on my encounter with a former MS-13 member from El Salvador, this article explores how gang members articulate their injuries within a transnational ethical field that renders the pain of the gang body unthinkable. It does so through an autoethnographic storytelling approach that also reveals the author’s own vulnerabilities as they surfaced in the surprising and affective terrain of research.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".