Language-specific grit: exploring psychometric properties, predictive validity, and differences across contexts
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
‘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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it