Bibliographic record
Abstract
This article proposes the concept of the wounded object as an approach to perceptions and representations of disability, wounding, and death, and in particular to ambivalent separations between the living and the dead, the human and the non-human, the singular and the multiple, the identified and the anonymous. My reading is informed by the unequally distributed proliferation of violence in our contemporary global landscape, through which some bodies and populations are designated as more disposable and closer to death than others. The asymmetrical processes of making-disposable take place in regions impacted by war as well as in many settings shaped by racism, economic exploitation and precarity. In this analysis I focus on the juxtaposition of two specific scenarios from Mexico and from the Mexico-US borderlands. The first of these is the statistical display of mortality rates produced by the Mexican government in the COVID-19 era. The second is a lithograph by contemporary artist Linda Lucia Santana, depicting the skull of Joaquín Murrieta, the nineteenth-century outlaw and lynching victim. While the first instance refers to a biopolitical model through which numerical data perform the obscuring of death or damage, the second suggests the enactment of sovereign power through the spectacle of a targeted killing, and thus performs a more explicit encounter with destruction. In each case, the wounded object, a troubled conjuring of past and continuing violence, offers evidence of diverse representations of damaged life, and a framework for the denunciation of both tangible and ephemeral injustices.
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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 | 0.002 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".