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Record W4386474008 · doi:10.33921/wnqo4570

An International Study of Democracy and Perceived Wellbeing

2021· article· en· W4386474008 on OpenAlexaffvenue
Alexandria Mungar, Kenneth M. Cramer

Bibliographic record

VenueJournal of Interpersonal Relations Intergroup Relations and Identity · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsHappinessDemocracyPer capita incomePer capitaDemographic economicsWorld Values SurveyLife satisfactionPersonal incomeTest (biology)PoliticsEconomicsPsychologyPolitical scienceEconomic growthSocioeconomicsDevelopment economicsSocial psychologyDemographySociologyPopulation

Abstract

fetched live from OpenAlex

The present study evaluated 85,000 respondents (from almost 60 nations) in the World Values Survey (Wave -6) and the link between perceived democracy and income brackets, plus their current state of health, happiness, and satisfaction with both life and finances. Mean scores for each nation then informed a secondary analysis by including GDP/capita. Results showed that income brackets were correlated to health and financial satisfaction while GDP/capita correlated with financial satisfaction among both high and lowincome levels. Multiple regression analyses confirmed the hypotheses: (a) that higher perceived democracy was positively related to wellbeing and health; and (b) that the relation between perceived democracy and wellbeing was moderated by income, with stronger correlations observed in both low-income and highincome (but not middle-income) nations. We conclude that democratic nations offer more personal and political freedoms, while securing better wages, income, and health care opportunities for their citizens. Future research is 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)0.003
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.355
Teacher spread0.337 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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