Collective Moral Imagination: Making Decisions for Persons With Dementia
Bibliographic record
Abstract
Much debate concerning 'precedent autonomy'--that is, the authority of former, competent selves to govern the welfare of later. non-competent selves--has assumed a radical discontinuity between selves, and has overlooked the 'bridging' role of intimate proxy decision-makers. I consider a recent proposal by Lynn et al. (1999) that presents a provocative alternative, foregrounding an imagined dialogue between the formerly competent patient and her/his trusted others. I consider what standards must be met for such dialogues to have moral force, appealing to narrative and feminist ethics. I then critique the dualistic construction of selves implicit in much of the advance directive literature, noting the continuities of dependence, character, and body, as well as the social dimension of the construction of selves.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".