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Record W2893727677 · doi:10.1007/s10902-018-0032-x

Perceived Religious Discrimination, Religiosity, and Life Satisfaction

2018· article· en· W2893727677 on OpenAlexaff
Zoua M. Vang, Feng Hou, Katharine Elder

Bibliographic record

VenueJournal of Happiness Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsStatistics CanadaMcGill University
Fundersnot available
KeywordsReligiosityLife satisfactionPsychologyReligious discriminationPositive psychologySocial psychologyQuality of Life ResearchReligious lifePublic healthTheologyReligious studies

Abstract

fetched live from OpenAlex

Using a large national representative survey, this study examines the effect of perceived religious discrimination, religiosity, and their interaction on life satisfaction. The results show that the negative effect of religious discrimination on life satisfaction is large and equivalent to the effects of some major life events such as widowhood and unemployment. Higher religiosity is associated with higher levels of life satisfaction and tends to mitigate the negative effect of experiencing religious discrimination. Furthermore, although the prevalence of perceived religious discrimination varies across major religious faiths, its negative effect on life satisfaction is generally consistent. The implications of the findings for future research and theoretical development on religious discrimination and its associations with subjective well-being are discussed.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.064
GPT teacher head0.389
Teacher spread0.326 · 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 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

Citations116
Published2018
Admission routes1
Has abstractyes

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