Hauntological dimensions of heart transplantation: the onto-epistemologies of deceased donation
Bibliographic record
Abstract
The practice of human organ transplantation studies is shot through with questions concerning the concepts of selfhood and identity that continually reach out towards transmigration, displacement and haunting. In particular, heart transplantation is the site at which the parameters of human life and death are tested to their limits, not simply for the recipient but for the donor too. In conventional biomedicine, the definition and therefore the moment of death is a matter of ongoing and disturbing dispute between two major channels of thought. Should we understand life to end at the point of cessation of cardiac function, or alternatively that of the brainstem? That whole logic is predicated, however, on the familiar binary of life/death that fails to address urgent concerns in three arenas: social-cultural imaginaries, postmodernist philosophy and increasingly exploratory bioscience. If there is always something about death that is uncanny, that exceeds rationalist thought, then we need to queer the concept and ask whether there are more sensitive ways of thinking the process of dying. The very concept of extended life for the recipient is no simple outcome, and the question of whose life has been prolonged is far from clear. My contribution touches on the idea of thinking transplantation in the mode of parasitism but will suggest an alternative Deleuzian way forward.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.075 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".