The many faces of self-control: Tacit assumptions and recommendations to deal with them.
Bibliographic record
Abstract
The term self-control is broadly used by both researchers and lay people. However, both the term itself and the research on self-control is full of assumptions that are often unexamined and unchallenged. In this paper, we question many assertions and assumptions about self-control that foster confusion and controversy, including the multitude of processes encompassed by the varied uses of the term “self-control.” We describe how these assumptions have caused gaps in the empirical literature, impeded the development of an interdisciplinary knowledge base about self-control, and ultimately slowed scientific progress in this area. Critically, we also present a set of recommendations for conducting research on self-control that would be relevant across theories, areas of inquiry, and disciplines. By bringing these assumptions to light, future research can better focus on issues that are important and foundational but have been relatively neglected by the literature because of their implicit nature. This paper thus raises new avenues for research by highlighting what the field generally assumes but does not test directly.
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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 | 0.097 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.106 |
| Scholarly communication | 0.019 | 0.051 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.020 | 0.042 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".