A Failure to Launch: Regulatory Modes and Boredom Proneness
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".