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Record W2969268239 · doi:10.1177/0968533219866235

Assisted dying for prison populations: Lessons from and for abroad

2019· article· en· W2969268239 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMedical Law International · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLegislationPrisonAssisted suicideLawPolitical scienceCriminologySociology

Abstract

fetched live from OpenAlex

Canadian federal legislation setting out the framework for medical assistance in dying (MAiD) in Canada came into effect in June 2016. Because of section 86(1) of the Corrections and Conditional Release Act, as soon as MAiD became available in the community, it also needed to be made available to federal prisoners. There are some good reasons to be concerned about MAiD in the Canadian corrections system based on logistical, legal, and moral considerations. Fortunately, Canada is not the first country to decriminalize assisted dying and so Canadian policies and practices can be compared to others and take some lessons from their experiences. Thus, by reviewing the legal status of assisted dying in prisons internationally, the regulation of assisted dying, demand for assisted dying from prisoners, and the process for prisoners accessing assisted dying, this article offers a comparative overview of assisted dying for prisoners around the world in an effort to inform Canadian and other jurisdictions’ law, policy, and practice.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.076
GPT teacher head0.437
Teacher spread0.361 · 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