Timing death: Entanglements of time and value at the end of life with dementia in the Netherlands
Bibliographic record
Abstract
This dissertation explores the entanglements of time and value at the end of life with dementia in the Netherlands. Interested in what people with dementia, their family members, and professional caregivers found important at the end of life, I explored how they sought to achieve a good death. I demonstrate that the moral value ascribed to death with dementia—the extent to which it may be welcomed or considered good—is rooted in the value ascribed to life with dementia. Further, I show that achieving a good death is subject to a range of temporal experiences and orientations. The different ways of making sense of and influencing the duration, speed, and significance of temporal aspects of dying can change how life and death are valued, not only in terms of a moral “good” but also in terms of its timing—whether death came at the “right” time. Managing the end of life then became a matter of producing and acting upon possible, alternative, or sometimes unwanted futures. Based on eighteen months of ethnographic fieldwork in nursing homes in the Netherlands and additional in-depth interviews in the home setting, I offer an in-depth understanding of how people may become oriented toward death, and the importance of time and future-making in managing the end of life with dementia. I argue that that the pursuit of a good death with dementia is both a temporal and a moral project.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".