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Record W4412453746 · doi:10.1177/00243639251356676

The Power of Words: Rebranding Euthanasia and Undermining Life's Sanctity

2025· article· en· W4412453746 on OpenAlexaboutno aff
J. J. Oh

Bibliographic record

VenueThe Linacre Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsDignityEnvironmental ethicsAssisted suicideAutonomyRight to dieSociologyVirtue ethicsCompassionLawPsychologyVirtuePolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Language shapes ethical perceptions, profoundly influencing societal attitudes toward euthanasia and physician-assisted suicide (PAS). Historically condemned by Western medicine as a violation of life's sanctity, euthanasia has undergone a dramatic transformation through deliberate linguistic reframing. Euphemisms such as "death with dignity" and "medical aid in dying" align euthanasia with values of compassion, autonomy, and patient empowerment, often overshadowing its moral implications for human dignity. This article critically examines how these linguistic shifts have influenced the normalization of euthanasia; broadened eligibility criteria; and facilitated its legal acceptance across nations like the Netherlands, Belgium, and Canada. Engaging secular arguments from consequentialism and autonomy-based ethics reveals their limitations, particularly their disregard for moral intentionality, relational responsibilities, and the intrinsic worth of life. Drawing on Catholic moral theology and virtue ethics, this study advocates for precise, transparent language alongside actionable policies, expanded palliative care, robust conscience protections, comprehensive public education, and stringent safeguards to protect vulnerable populations and resist ethical erosion. Reclaiming ethical clarity requires more than abstract discussion - it demands bold action in language and policy to counter euphemistic normalization and affirm the inherent dignity of human life.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.019
GPT teacher head0.309
Teacher spread0.290 · 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 designQualitative
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

Citations2
Published2025
Admission routes1
Has abstractyes

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