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Record W3112418321 · doi:10.3390/ijerph17239116

Boredom Proneness and Self-Control as Unique Risk Factors in Achievement Settings

2020· article· en· W3112418321 on OpenAlexafffund
Jhotisha Mugon, James Boylan, James Danckert

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBoredomPsychologyFeelingTraitSelf-controlSocial psychologyDevelopmental psychologyClinical psychologyComputer science

Abstract

fetched live from OpenAlex

The state of boredom arises when we have the desire to be engaged in goal pursuit, but for whatever reason we cannot fulfil that desire. Boredom proneness is characterized by both frequent and intense feelings of boredom and is an enduring individual difference trait associated with a raft of negative outcomes. There has been some work in educational settings, but relatively little is known about the consequences of boredom proneness for learning. Here we explored the unique contributions of boredom proneness, self-control and self-esteem to undergraduate self-reported higher grade point average (GPA). Within educational settings, prior research has shown self-control and self-esteem to be associated with better academic performance. In contrast, boredom proneness is associated with lower levels of self-control and self-esteem. Our analyses replicate those previous findings showing that self-control acts as a positive predictor of GPA. Importantly, we further demonstrated, for the first time, that boredom proneness has a unique contribution to GPA over and above the contribution of self-control, such that as boredom proneness increases, GPA decreases. We discuss potential mechanisms through which boredom proneness may influence academic performance.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.248

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.057
GPT teacher head0.347
Teacher spread0.290 · 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

Citations44
Published2020
Admission routes2
Has abstractyes

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