The Temporal Ordering of Motivation and Self-Control: A Cross-Lagged Effects Model
Bibliographic record
Abstract
Mechanisms leading to cognitive energy depletion in performance settings such as high-level sports highlight likely associations between individuals' self-control capacity and their motivation. Investigating the temporal ordering of these concepts combining self-determination theory and psychosocial self-control theories, the authors hypothesized that athletes' self-control capacity would be more influenced by their motivation than vice versa and that autonomous and controlled types of motivation would predict self-control capacity positively and negatively, respectively. High-level winter-sport athletes from Norwegian elite sport colleges (N = 321; 16-20 years) consented to participate. Using Bayesian structural equation modeling and 3-wave analyses, findings revealed credible self-control → motivation → self-control cross-lagged effects. Athletes' trait self-control especially initiated the temporal ordering of the least controlled types of motivation (i.e., intrinsic, integrated, and amotivation). Findings indicate that practicing self-control competencies and promoting athletes' autonomous types of motivation are important components in the development toward the elite level. These components will help athletes maintain their persistent goal striving by increasing the value and inherent satisfaction of the development process, avoiding the debilitating effects of self-control depletion and exhaustion.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".