Behavioral and Experiential Self-Regulations in Psychological Well-Being under Proximal and Distal Goal Conditions
Bibliographic record
Abstract
This study examined the relationship of goal-related components of cybernetic, behavioral, and experiential self-regulations to psychological well-being under two types of conditions, the pursuit of intrinsic goals in general and specific intrinsic goals for the academic term. In an online survey, undergraduates (N = 186) completed global measures of psychological well-being, behavioral and experiential self-regulations, and rated themselves on goal-related self-regulatory components. Correlations indicated that most of the cybernetic, behavioral and experiential self-regulatory variables were associated with each other and with well-being. In terms of the goal-related self-regulatory components, when pursuing intrinsic goals more generally, the experiential self-regulatory component of enjoyment of the activity predicted well-being. However, when pursuing intrinsic term goals, the cybernetic self-regulatory component of perceived goal progress and the behavioral self-regulatory component of self-reinforcement for goal progress predicted well-being. The findings extend theoretical conceptualizations of psychological well-being by integrating compatibilities between cybernetic, behavioral, experiential self-regulatory processes and motivational conditions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".