Keep on Truckin’ or Stay the Course? Exploring Grit Dimensions as Differential Predictors of Educational Achievement, Satisfaction, and Intentions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an ongoing effort to identify predictors of educational success and achievement, grit has emerged as a seemingly useful disposition. Grit is conceived as the combination of perseverance of effort and consistency of interest over time, but the predictive utility of these two dimensions has rarely been explored separately, and the limited research available has considered a small number of outcomes. This article draws upon three samples at two universities to examine the relationships between grit dimensions and various student outcomes. Multiple regression results indicated that perseverance of effort predicted greater academic adjustment, college grade point average, college satisfaction, sense of belonging, faculty–student interactions, and intent to persist, while it was inversely related to intent to change majors. Consistency of interest was associated with less intent to change majors and careers, but it was not significantly associated with any other outcome in the expected direction when controlling for other variables.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it