Moving beyond Inhibition: Capturing a Broader Scope of the Self-Control Construct with the Self-Control Strategy Scale (SCSS)
Bibliographic record
Abstract
The purpose of the present research was to develop a more comprehensive measure of self-control that reflects recent theoretical advancements that extend beyond inhibition. Across six samples (N = 1,946, 48.95% males, Ages 18-76, US-MTurkers/Israelis), we sought to develop and validate the Self-Control Strategies Scale (SCSS), as well as examine its predictive validity across important life domains (e.g., weight, physical activity, savings). The SCSS is comprised of eight self-control strategies that represent three categories: anticipatory control (situation selection, reward, punishment, pre-commitment), down-regulation of temptation (distraction, cognitive change, acceptance), and behavioral inhibition. Results indicate that there was a strong association between the widely used Brief Self-Control Scale (BSCS). and the behavioral inhibition strategy of the SCSS. While the behavioral inhibition strategy was a strong and consistent predictor of most self-control related outcomes, results further indicate that in some domains, but not others, certain strategies may be beneficial whereas others may be detrimental. While inhibition remains to be an important factor of self-control, our findings point to the importance of adapting the use of different strategies to different domains. The SCSS can therefore be used to gain a more fine-grained understanding of the self-control construct.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".