It is Not OK to Die (With Dignity) in America: An Analysis of the United States’ Denaturalization of Death and its Intimate Relationship with the SARS-CoV-2 Pandemic
Bibliographic record
Abstract
The SARS-CoV-2 pandemic (COVID) transformed the everyday life of millions around the globe; however, many Americans never believed the virus—or the threat of death—was real to begin with. This research analyzes a systemic denaturalization of death and its processes within the United States, and subsequently identifies a public health crisis stemming from the biomedicalization of aging, illusory expectations of end-of-life care, and a generational pursuit to achieve a ‘good death’ within a capitalist economy. Informed by over 1,300 hours of first-hand participant observation in two public health institutions in Indiana and interviews with medical providers discussing the impact their own (de)naturalization of death on patient care, this paper dissects American’s social and cultural behavior regarding death, its processes, and its intimate connection to the SARS-CoV2 pandemic. While most Americans describe their worst nightmare for end of life, they are most often suffering and dying exactly as they fear: institutionalized and isolated. This explores why this is so.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".