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Record W4367048460 · doi:10.1136/jme-2022-108871

Choosing death in unjust conditions: hope, autonomy and harm reduction

2023· article· en· W4367048460 on OpenAlexaffabout
Kayla Wiebe, Amy Mullin

Bibliographic record

VenueJournal of Medical Ethics · 2023
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAutonomyArgument (complex analysis)InjusticeHarmContext (archaeology)SociologyLaw and economicsLawSocial psychologyPsychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

In this essay, we consider questions arising from cases in which people request medical assistance in dying (MAiD) in unjust social circumstances. We develop our argument by asking two questions. First, can decisions made in the context of unjust social circumstance be meaningfully autonomous? We understand 'unjust social circumstances' to be circumstances in which people do not have meaningful access to the range of options to which they are entitled and 'autonomy' as self-governance in the service of personally meaningful goals, values and commitments. People in these circumstances would choose otherwise, were conditions more just. We consider and reject arguments that the autonomy of people choosing death in the context of injustice is necessarily reduced, either by restricting their options for self-determination, through their internalisation of oppressive attitudes or by undermining their hope to the point that they despair.Second, should MAiD be available to people in such circumstances, even when a sound argument can be made that the agents in question are autonomous? In response, we use a harm reduction approach, arguing that even though such decisions are tragic, MAiD should be available. Our argument engages with relational theories of autonomy as well as recent criticism raised against them and is intended to be general in application, although it emerges in response to the Canadian legal regimen around MAiD, with a focus on recent changes in Canada's eligibility criteria to qualify for MAiD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.078
GPT teacher head0.427
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations44
Published2023
Admission routes2
Has abstractyes

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