Bibliographic record
Abstract
Trait self-control is highly valued, often equated with moral righteousness and associated with numerous positive life outcomes. This paper challenges the conventional conflation of trait self-control and state self-control. We suggest that while trait self-control is consistently linked to success, state self-control is not the causal mechanism driving these benefits. Trait self-control, sometimes also referred to as conscientiousness, grit, and the ability to delay gratification, predicts better health, wealth, and academic achievement. Conventional wisdom has it that people high in trait self-control reap all these benefits because they engage in more state self-control, defined as the momentary act of resolving conflict between goals and fleeting desires. Despite its intuitive appeal, there are problems with extolling state self-control because of our love for trait self-control. First, empirical evidence suggests that individuals high in trait self-control do not engage in more state self-control but engage it less. Second, changes to state self-control do not reliably and sustainably improve people's outcomes, as least in the long-term. And third, despite the possibility of dramatic improvements in trait self-control, these improvements are often short lived, with people returning to their baseline trait level over longer time horizons. The roots of this problem are numerous: Imprecise and inaccurate naming of our constructs that lead to construct drift and contamination; ignoring the numerous other facets of conscientiousness like orderliness or industriousness; and not appreciating that traits are sometimes not reducible to states. We suggest that the celebrated benefits of trait self-control arise from mechanisms beyond state self-control and highlight the need for a broader conceptualization of self-control in psychological research and practical interventions.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".