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Record W2319126308 · doi:10.1177/1948550614568161

Higher Income Is Associated With Less Daily Sadness but not More Daily Happiness

2015· article· en· W2319126308 on OpenAlex
Kostadin Kushlev, Elizabeth W. Dunn, Richard E. Lucas

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Psychological and Personality Science · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSadnessHappinessPsychologySocial psychologyDevelopmental psychologyPopulationCausality (physics)AngerDemographySociology

Abstract

fetched live from OpenAlex

Although extensive previous research has explored the relationship between income and happiness, no large-scale research has ever examined the relationship between income and sadness. Yet, happiness and sadness are distinct emotional states, rather than diametric opposites, and past research points to the possibility that wealth may have a greater impact on sadness than happiness. Using data from a diverse cross section of the U.S. population ( N = 12,291), we show that higher income is associated with experiencing less daily sadness, but has no bearing on daily happiness. This pattern of findings could not be explained by relevant demographics, stress, and people’s daily time use. Although causality cannot be inferred from this correlational data set, the present findings point to the possibility that money may be a more effective tool for reducing sadness than enhancing happiness.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.140
GPT teacher head0.387
Teacher spread0.247 · 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