Bibliographic record
Abstract
I focus on two contemporary art installations in which Teresa Margolles employs water used to wash corpses during autopsies. By running this water through a fog machine or through air conditioners, these works incorporate bodily matter but refuse to depict, identify or locate anybody (or any body) within it. Rather, Margolles creates abstract works in which physical limits – whether of bodies or of art works – dissolve into a state of indeterminacy. With that pervasive distribution of corporeal matter, Margolles charts the dissolution of the social, political and spatial borders that contain death from the public sphere. In discussing these works, I consider Margolles’ practice in relation to the social and aesthetic function of the morgue. Specifically, I consider how Margolles turns the morgue inside out, opening it upon the city in order to explore the inoperative distinctions between spaces of sociality and those of death. In turn, I consider how Margolles places viewers in uneasy proximity to mortality, bodily abjection and violence in order to illustrate the social, political and aesthetic conditions by which bodies become unidentifiable. I ultimately argue that her aesthetic strategies match her ethical aspirations to reconsider relations to death, violence and loss within the social realm.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".