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Record W4403998227 · doi:10.1002/hast.4910

Language Matters: The Semantics and Politics of “Assisted Dying”

2024· article· en· W4403998227 on OpenAlexaboutno aff
Anna Magdalena Elsner, Charlotte E. Frank, Marc Keller, Jordan O. McCullough, Vanessa Rampton

Bibliographic record

VenueThe Hastings Center Report · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
FundersH2020 European Research CouncilStaatssekretariat für Bildung, Forschung und InnovationHorizon 2020 Framework Programme
KeywordsPoliticsSemantics (computer science)LinguisticsSociologyPolitical scienceComputer sciencePhilosophyProgramming languageLaw

Abstract

fetched live from OpenAlex

This essay examines the impact of linguistic choices on the perception and regulation of assisted dying, particularly in Canada. It argues that euphemistic terms like "medical assistance in dying" and its acronym, "MAID," serve to normalize the practice, potentially obscuring its moral gravity. This contrasts with what is seen in Belgium and the Netherlands, where terms like "euthanasia" are used, as well as in France and the United Kingdom, where terminology remains divisive and contested. By tracing the evolution of these terms and what they reveal about different cultural and legal approaches, this essay sheds light on the politics of language in end-of-life discourses. It suggests that the shift toward euphemistic language reflects a broader discomfort with death that can shape public attitudes and legal frameworks. It calls for a more transparent, philosophically grounded approach to terminology and suggests that continued debate about semantics is necessary to capture the complexities and ethical significance of assisted dying.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.171

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.089
GPT teacher head0.394
Teacher spread0.305 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

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