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Record W2785019768 · doi:10.1111/bjso.12243

Giving in when feeling less good: Procrastination, action control, and social temptations

2018· article· en· W2785019768 on OpenAlexafffund
Fuschia M. Sirois, Benjamin Giguère

Bibliographic record

VenueBritish Journal of Social Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsProcrastinationPsychologySocial psychologyFeelingAffect (linguistics)MoodMediationPersonalityContext (archaeology)Experience sampling methodTraitDevelopmental psychologyModerated mediation

Abstract

fetched live from OpenAlex

Emotion-regulation perspectives on procrastination highlighting the primacy of short-term mood regulation focus mainly on negative affect. Positive affect, however, has received much less attention and has not been considered with respect to social temptations. To address this issue, we examined how trait procrastination was linked to positive and negative affect in the context of social temptations across two prospective studies. Action Control Theory, Personality Systems Interactions Theory, and a mood regulation theory of procrastination served as guiding conceptual frameworks. In Study 1, moderated mediation analyses revealed that low positive affect explained the link between trait procrastination and time spent procrastinating on academic tasks over a 48-hr period in a student sample (N = 142), and this effect was moderated by the presence of social temptations. Parallel results for goal enjoyment assessed at Time 2 were found in Study 2 with a community sample (N = 94) attempting to make intended health behaviour changes over a 6-month period. Our findings indicate that procrastinators are at risk for disengaging from intended tasks when social temptations are present and positive task-related affect is low.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.368
Teacher spread0.324 · 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.

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

Citations54
Published2018
Admission routes2
Has abstractyes

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