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Record W2782099281 · doi:10.1177/0020715218787582

Public support for the right to euthanasia: Impact of traditional religiosity and autonomy values across 37 nations

2018· article· en· W2782099281 on OpenAlexvenueno aff
Maksim Rudnev, Aleksandra Savelkaeva

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2018
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsnot available
Fundersnot available
KeywordsReligiosityAutonomyWorld Values SurveyValue (mathematics)Social psychologyPreferencePsychologyPerspective (graphical)Multilevel modelSociologyPolitical scienceLawEconomicsStatisticsMicroeconomics

Abstract

fetched live from OpenAlex

This article takes a postmodernization perspective on support for the right to euthanasia by treating it as an expression of a process of value change, as a preference for quality over quantity of life. Using the data from the fifth wave of the World Values Survey, this study attempts to answer the question of whether the mass support for the right to euthanasia is an expression of autonomy values rather than just a function of a low religiosity. Multilevel regressions demonstrate that both traditional religiosity and autonomy values have a high impact at the individual level, while at the country level only the effects of traditional religiosity are significant. Autonomy values have stronger association with attitudes to euthanasia in countries with higher levels of postmaterialism. Multilevel path analysis demonstrates that the effect of religiosity is partially and weakly mediated by the values of autonomy at both levels. Although religiosity was found to have a much stronger impact, the independent effect of autonomy values suggests that mass support for the right to euthanasia is a value-driven preference for quality over quantity of life. We conclude by suggesting that the fall in traditional religiosity might emphasize the role of values in moral attitudes regulation.

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 score0.848
Threshold uncertainty score0.918

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 studies0.0000.001
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 head0.297
GPT teacher head0.494
Teacher spread0.198 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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