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Record W2151307134 · doi:10.1177/001979391306600503

When Does Money Make Money More Important? Survey and Experimental Evidence

2013· article· en· W2151307134 on OpenAlexaff
Sanford E. DeVoe, Jeffrey Pfeffer, Byron Y. Lee

Bibliographic record

VenueIndustrial and Labor Relations Review · 2013
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAffect (linguistics)EconomicsDifferential (mechanical device)Monetary economicsDemographic economicsLabour economicsPsychology

Abstract

fetched live from OpenAlex

The authors investigate how the amount and source of income affects the importance placed on money. Using a longitudinal analysis of the British Household Panel Survey and evidence from two laboratory experiments, they found that larger amounts of money received for labor were associated with individuals placing greater importance on money; but this effect did not hold for money not related to work. The longitudinal survey analysis demonstrated these differential effects of the source of income on money's importance while holding constant stable individual differences. The experiments provide causal evidence that the source of income has an effect on the importance of money as well as on the effort expended to earn more money. The authors' results suggest that, even as individual differences in the importance placed on money may affect peoples' income, depending on its source, income can also affect the importance people place on money.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.088
GPT teacher head0.363
Teacher spread0.275 · 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 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

Citations29
Published2013
Admission routes1
Has abstractyes

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