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Record W2971502043 · doi:10.1037/pspp0000261

Understanding short-term variability in life satisfaction: The Individual Differences in Evaluating Life Satisfaction (IDELS) model.

2019· article· en· W2971502043 on OpenAlexfundno aff
Emily C Willroth, Oliver P. John, Jeremy C. Biesanz, Iris B. Mauss

Bibliographic record

VenueJournal of Personality and Social Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsnot available
FundersNational Institute of Mental HealthSocial Sciences and Humanities Research Council of CanadaNational Institute on AgingNational Institutes of Health
KeywordsLife satisfactionPsychologyPsycINFOHappinessNeuroticismSocial psychologyTerm (time)Developmental psychologyClinical psychologyPersonalityMEDLINE

Abstract

fetched live from OpenAlex

= 536), we assessed life satisfaction and emotions daily or multiple times per day for 2 weeks. We tested 4 hypotheses derived from the IDELS model. First, participants differed substantially in the degree of short-term variability in life satisfaction, and these individual differences were moderately stable. Second, participants differed substantially in emotion globalizing, and these individual differences were moderately stable. Third, higher emotion globalizing predicted greater short-term variability in life satisfaction. Fourth, greater short-term variability in life satisfaction was associated with a maladaptive profile of greater neuroticism and worse psychological health. We discuss implications for life satisfaction theory and measurement. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.315
GPT teacher head0.419
Teacher spread0.104 · 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 designTheoretical or conceptual
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

Citations47
Published2019
Admission routes1
Has abstractyes

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