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Record W2808150063 · doi:10.3389/fpsyg.2018.01126

A Failure to Launch: Regulatory Modes and Boredom Proneness

2018· article· en· W2808150063 on OpenAlexafffund
Jhotisha Mugon, Andriy A. Struk, James Danckert

Bibliographic record

VenueFrontiers in Psychology · 2018
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBoredomPsychologyCognitive psychologySocial psychology

Abstract

fetched live from OpenAlex

Boredom is a ubiquitous human experience characterized as a state of wanting but failing to engage with the world. Individuals prone to the experience of boredom demonstrate lower levels of self-control which may be at the heart of their failures to engage in goal-directed, meaningful behaviours. Here we develop the hypothesis that distinct self-regulatory profiles, which in turn differentially influence modes of goal pursuit, are at the heart of boredom proneness. Two specific regulatory modes are addressed: Locomotion, the desire to ‘just do it’, an action oriented mode of goal-pursuit, and Assessment, the desire to ‘do the right thing’, an evaluative orientation towards goal pursuit. We present data from a series of seven large samples of undergraduates showing that boredom proneness is negatively correlated with Locomotion, as though getting on with things acts as a prophylactic against boredom. This ‘failure to launch’ that we suggest is prevalent in the highly boredom prone individual, could be due to an inability to appropriately discriminate value (i.e., everything is tarred with the same grey brush), an unwillingness to put in the required effort to engage, or simply a failure to get started. In contrast, boredom proneness was consistently positively correlated with the Assessment mode of self-regulation. We suggest that this association reflects a kind of rumination that hampers satisfying goal pursuit.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.027
GPT teacher head0.302
Teacher spread0.276 · 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 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

Citations113
Published2018
Admission routes2
Has abstractyes

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