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Record W2319446422 · doi:10.1177/1073191111408229

The Measurement of Boredom

2011· article· en· W2319446422 on OpenAlexaff
Kimberley B. Mercer-Lynn, David B. Flora, Shelley A. Fahlman, John D. Eastwood

Bibliographic record

VenueAssessment · 2011
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsYork University
Fundersnot available
KeywordsBoredomPsychologyImpulsivityExperiential avoidanceNeuroticismDysphoriaClinical psychologyAnxietyPersonalityDevelopmental psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

There are two commonly used measures of boredom: the Boredom Proneness Scale (BPS) and the Boredom Susceptibility Scale (ZBS). Although both were designed to measure the propensity to experience boredom (i.e., trait boredom), there are reasons to think they may not measure the same construct. The present research sought to evaluate this proposition in several stages. Specifically, relationships between the BPS, ZBS, and important causal (Study 1, N = 837), correlational (Study 2, N = 233), and outcome variables (Study 3, N = 137) were examined in university students. Taken together, results support the notion that the BPS and ZBS do not measure the same construct. Specifically, higher BPS scores were associated with higher levels of neuroticism, experiential avoidance, attentional and nonplanning impulsivity, anxiety, depression, dysphoria, and emotional eating. Conversely, higher ZBS scores were associated with higher levels of motor impulsivity, sensitivity to reward, gambling, and alcohol use and lower levels of neuroticism, experiential avoidance, and sensitivity to punishment.

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)0.015
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.144
GPT teacher head0.308
Teacher spread0.164 · 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

Citations125
Published2011
Admission routes1
Has abstractyes

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