Bibliographic record
Abstract
In this essay, I take up the question of the time of medicine in relation to two events in the U.S. from 2005—the Terri Schiavo case and Hurricane Katrina and its aftermath. I consider both cases as “mediatized medical events,” that is, as events in which the practices of medicine received considerable media attention at a particular historical moment; or, we might say, as events that brought a convergence between media and medical practices. I juxtapose these two events because, placed side by side, they help make visible two stories of catastrophe, as well as the many difficulties of telling stories of catastrophe. Bringing together these seemingly divergent events allows me to draw connections that I hope will expand our bioethical imaginary beyond the reductive approaches that tend to dominate the practice of bioethics today. I also juxtapose them to signal a bioethical tension at the heart of the neoliberal state’s response to catastrophe in general, what Foucault might have diagnosed as the difference between making live and letting die. In these two events, we glimpsed—if only fleetingly—the state’s operation of making live and letting die, and medicine’s central role in that operation, as well as the re-assertion of medical sovereignty in crisis events.
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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".