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Record W2036578863 · doi:10.1080/17439760.2012.713504

It is about time: Daily relationships between temporal perspective and well-being

2012· article· en· W2036578863 on OpenAlexaff
Jonathan Rush, Frédérick M. E. Grouzet

Bibliographic record

VenueThe Journal of Positive Psychology · 2012
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPerspective (graphical)PsychologyTime perspectiveDimension (graph theory)Dynamics (music)Experience sampling methodWell-beingActivities of daily livingCognitive psychologySocial psychologyComputer scienceArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

This study examined the day-to-day relationships between temporal perspective and well-being. Temporal perspective has predominantly been measured with single-occasion measurement designs, which ignore the potential for within-person variations that may be important in accounting for fluctuations in well-being. A 14-day daily diary design was employed to examine the dimensions of temporal perspective (temporal focus, temporal attitude, and temporal distance) and their dynamic relationships with daily well-being. The results from multilevel analyses indicated that: (a) there is evidence of within-person variability in daily temporal perspective, and (b) this within-person variability in temporal perspective fluctuated systematically with fluctuations in daily well-being. Each temporal perspective dimension was useful in predicting daily well-being. Temporal perspective dimensions interacted with each other such that the daily relationships with well-being depended on both the temporal region (past, present, or future) and the nature of the thoughts (pleasant vs. unpleasant; near vs. far).

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)0.007
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
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 head0.068
GPT teacher head0.414
Teacher spread0.346 · 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

Citations57
Published2012
Admission routes1
Has abstractyes

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