Pluralistic death as a reconciliation of the death standards debate
Bibliographic record
Abstract
The definition of death has long been fertile ground for philosophical and ethical debate. Attempts to ontologize death have been complicated by conceptual ambiguities, normative tensions, and conflicting intuitions about which physical or metaphysical features are relevant to diagnosing death. For example, mid-20th-century advances in ventilation and resuscitation technologies facilitated the shift from a cardiopulmonary to a brain-based standard of death. Rather than resolving the debate, this shift raised new questions about intermediary states like comas, and exposed deeper uncertainties about what truly constitutes the end of a human life. Contemporary debates have not escaped this struggle: in our aging global society, patients with advanced neurodegeneration present a new dimension to the challenge of defining death.In this thesis, I argue that a pluralistic framework—one that accommodates multiple coexisting standards of death—best reconciles our diverse and complex intuitions around death. I illustrate this through an interpretation of Martin Heidegger’s tripartite structure of death as an exemplar of a coherent and clinically relevant pluralistic model.Chapter 1 traces the evolution from cardiopulmonary death to brain death to metaphysical standards of death. I argue that while each framework is useful, no single framework unilaterally captures the full range of complex intuitions around death. In describing this evolution, I explicate that death is not a value-neutral concept and that our normative commitments guide its ontology. If ethics leads ontology, then multiple ethical intuitions should support multiple valid definitions of death.Chapter 2 develops a clinically grounded version of Heidegger’s tripartite model—physical death, death of personal identity, and death of futurity—and shows how this pluralistic framework is ethically relevant. By illustrating the ethical relevance of this tripartite Heideggerian framework, I demonstrate how a pluralistic approach to death can successfully account for the complexity of intuitions around death, resolving the issues encountered by the unitary frameworks discussed in Chapter 1. Finally, using a case study, I show how this tripartite framework (and thus pluralistic death more broadly) bears implications for end-of-life care.Ultimately, I suggest that embracing pluralistic frameworks of death can both reconcile the death standards debate and enable the public to make maximally beneficial end-of-life care decisions for themselves and their loved ones
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.053 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".