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Record W4388457808 · doi:10.1111/bjso.12695

Why do we never have enough time? Economic inequality fuels the perception of time poverty by aggravating status anxiety

2023· article· en· W4388457808 on OpenAlexaff
Q Zhao, Rongzi Ma, Zhenzhen Liu, Tianxin Wang, Xiaomin Sun, Jan‐Willem van Prooijen, Mengxi Dong, Yue Yuan

Bibliographic record

VenueBritish Journal of Social Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsPovertyInequalityPsychologyEconomic inequalityPerceptionAnxietySocial psychologyTest (biology)Demographic economicsSocial inequalityEconomicsEconomic growth

Abstract

fetched live from OpenAlex

People in many societies report that they do not have enough time. What makes people feel so rushed? We propose that economic inequality leads to perceived time poverty by increasing status anxiety. Five studies examined this line of reasoning. Study 1 (N = 230) found a positive correlation between economic inequality and perceived time poverty. Study 2 (N = 194) manipulated economic inequality to test the causal link between economic inequality and perceived time poverty. The results showed that people perceived more time poverty in the high (vs. low) economic inequality condition. Study 3 (N = 381) supported the mediating role of status anxiety in the relationship between economic inequality and perceived time poverty using a questionnaire survey. Study 4 (pre-registered; N = 283) manipulated economic inequality in an ecological valid way and yielded further support for the hypotheses. In pre-registered Study 5 (N = 233), a blockage manipulation design was employed to test the mediating effect of status anxiety as a function of economic inequality, which provided causal evidence for the proposed mediator. Our findings suggest that economic inequality serves as a structural societal factor that fuels people's perception of time poverty.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.024
GPT teacher head0.335
Teacher spread0.311 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations16
Published2023
Admission routes1
Has abstractyes

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