Bibliographic record
Abstract
Abstract Research on self‐control has flourished within the last two decades, with many researchers trying to answer one of the most fundamental questions regarding human behaviour—how do we successfully regulate desires in the pursuit of long‐term goals? While recent research has focused on different strategies to enhance self‐control success, we still know very little about how strategies are implemented or where the need for self‐control comes from in the first place. Drawing from parallel fields (e.g., emotion regulation, health) and other theories of self‐regulation, we propose an integrative framework that describes self‐control as a dynamic, multi‐stage process that unfolds over time. In this review, we first provide an overview of this framework, which poses three stages of regulation: the identification of the need for self‐control, the selection of strategies to regulate temptations, and the implementation of chosen strategies. These regulatory stages are then flexibly monitored over time. We then expand this framework by outlining a series of growth points to guide future research. By bridging across theories and disciplines, the present framework improves our understanding of how self‐control unfolds in everyday life.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".