Language-specific grit: exploring psychometric properties, predictive validity, and differences across contexts
Bibliographic record
Abstract
‘Grit’ has been identified as an important predictor of success in a number of academic and non-academic domains (Duckworth, A. L., C. Peterson, M. D. Matthews, and D. R. Kelly. 2007. “Grit: Perseverance and Passion for Long-Term Goals.” Journal of Personality and Social Psychology 92: 1087–1101. doi:10.1037/0022-3514.92.6.1087). The present study responds to calls to examine language-specific (L2) grit. We investigated the factor structure of the L2-Grit Scale (Teimouri, Y., L. Plonsky, and F. Tabandeh. in press. “L2 Grit: Passion and Perseverance for Second-Language Learning.” Language Teaching Research, 1–26. doi:10.1177/1362168820921895) and examined the predictive validity of grit and three other individual differences in relation to English proficiency among second and foreign language learners from different countries. Factor analysis revealed a two-dimensional structure of the L2-Grit Scale. However, the correlation between the factors was stronger in the EFL than in the ESL sample. Moreover, the L2 grit subscales had differential predictive validity: Perseverance of Effort was a significant positive predictor of proficiency in the EFL context, while Consistency of Interest was a significant negative predictor in the ESL context. This study represents one of the first inquiries into L2 grit and how it relates to the learning context in particular.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".