Examining the unique and combined effects of grit, trait self-control, and conscientiousness in predicting motivation for academic goals: A commonality analysis
Bibliographic record
Abstract
The purpose of the present research was to examine the predicative ability of both the unique and combined components of grit, trait self-control, and conscientiousness in the context of academic goal pursuit. Participants (n1=163, n2=551) were asked to complete assessments of each self-regulatory trait. They also identified three goals that they planned to pursue over the next year and rated their motivation for pursuing them, of which we retained only academic goals. Together, grit, trait self-control, and conscientiousness explained 9.9% of the variance in academic goal motivation across both samples. Using commonality analysis, we found that the overlapping components of grit, trait self-control, and conscientiousness accounted for 49.6% of the explained variance (4.9% of the total variance), with the individual components each accounting for less than 20% (2% of the total variance). Implications and suggestions for subsequent research on self-regulatory traits are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".