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Record W4232276961 · doi:10.31234/osf.io/58g2m

Rich Environments, Dull Experiences: How Environment Can Exacerbate the Effect of Constraint on the Experience of Boredom

2020· preprint· en· W4232276961 on OpenAlexaff
Andriy A. Struk, Abigail A. Scholer, James Danckert, Paul Seli

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBoredomAffordancePsychologyLaptopSocial psychologyConstraint (computer-aided design)Control (management)Value (mathematics)Cognitive psychologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

We examined the hypothesis that boredom is likely to occur when opportunity costs are high; that is, when there is a high potential value of engaging in activities other than the researcher-assigned activity. To this end, participants were either placed in a room with many possible affordances (e.g., a laptop, puzzle, etc.; affordances condition; n = 121), or they were ushered into an empty room (control condition; n = 107). In both conditions participants were instructed to entertain themselves with only their thoughts (hence, participants in the affordances condition were to refrain from engaging with the available options). As predicted, participants in the affordances condition reported higher levels of boredom compared with those in the control condition. Results suggest, that under some conditions, environments that afford alternative activities may be more boring than those that are void of such activities.

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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.003
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 head0.034
GPT teacher head0.239
Teacher spread0.205 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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