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Record W2605746811 · doi:10.1111/bioe.12353

Assisted Dying & Disability

2017· article· en· W2605746811 on OpenAlexaboutno aff
Christopher A. Riddle

Bibliographic record

VenueBioethics · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
FundersFondation BrocherSan Diego State University
KeywordsLegalizationAutonomyDenialAssisted suicideOpposition (politics)LegislationDisabled peopleVulnerability (computing)BioethicsPolitical sciencePsychologySociologyLawMedicinePsychiatryPoliticsPsychotherapist

Abstract

fetched live from OpenAlex

This article explores at least two dominant critiques of assisted dying from a disability rights perspective. In spite of these critiques, I conclude that assisted dying ought to be permissible. I arrive at the conclusion that if we respect and value people with disabilities, we ought to permit assisted dying. I do so in the following manner. First, I examine recent changes in legislation that have occurred since the Royal Society of Canada Expert Panel on End-of-Life Decision-Making report, published in this journal. I suggest that these changes are likely to only strengthen opposition to assisted dying from disability rights activists and people with disabilities. Second, I focus on respect for people with disabilities and in particular, respect for their autonomy and decision-making abilities. Third, I explore the opposition to assisted dying that focuses on risk and the vulnerability of people with disabilities. Here I suggest that this risk ought not to be of special concern. Ultimately, I conclude that upholding respect for the disabled requires the legalization of assisted dying, rather than the denial of access in a misguided effort to protect people with disabilities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.018
Scholarly communication0.0070.005
Open science0.0010.006
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0260.004

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 head0.559
GPT teacher head0.545
Teacher spread0.014 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations19
Published2017
Admission routes1
Has abstractyes

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