The Dynamics of Self-Control Conflicts in Daily Life in Predicting Self-Control Success and Perceived Self-Regulatory Effectiveness
Bibliographic record
Abstract
People often face conflicts where they must choose between their long-term goals and tempting alternatives. Using an open-ended daily diary design, we investigated the characteristics of self-control conflicts in daily life, both replicating and extending past work. Specifically, we examined the factors that affected self-control conflict success, as well as how the nature and resolution of the conflict affected general perceptions of self-regulatory effectiveness. Self-control conflicts varied considerably within-persons including the domain of the conflict, the use of strategies, and whether they were successfully resolved. There was also variability in people’s subjective perceptions of how pulled they felt towards the temptation and the opposing goal, as well as how difficult and important the overall decision was. Furthermore, these factors predicted whether a conflict was resolved successfully (i.e., in favor of the goal), with pull towards the temptation emerging as the strongest predictor. People were also more successful in resolving self-control conflicts when they reported using any type of self-regulatory strategy; no specific strategy emerged as most effective. On days when participants successfully resolved conflicts, they also felt more confident in their general ability to self-regulate. Overall, our findings largely conceptually replicate past work using an open-ended diary format, and suggest that factors influencing self-control conflict resolution are also linked to general feelings of self-regulatory effectiveness.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".