Trait Boredom as a Lack of Agency: A Theoretical Model and a New Assessment Tool
Bibliographic record
Abstract
Trait boredom plays a significant role in well-being. However, this construct suffers from conceptual ambiguity and measurement problems. The aim of this study was to propose a comprehensive theory and a strong assessment tool to address these limitations. We defined trait boredom as the frequent experience of state boredom resulting from a chronic lack of agency. We developed a six-item self-report scale of the tendency to often experience boredom. Results confirmed a uni-dimensional scale with strong psychometric properties, including adequate internal consistency (ω = .84-.93), interindividual stability (69.04% of variance accounted by a trait factor), and acceptable model fit (CFI = .977-.998, TLI = .962-.997, RMSEA = .025-.090, SRMR = .014-.029). Results confirmed the validity of the scale by showing its associations with related measures. Our findings provide clarity on trait boredom and a strong, new measure to be used in future work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".