Development and Initial Validation of the L2‐Teacher Grit Scale
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—“perseverance and passion for long‐term goals” (Duckworth, Peterson, Matthews, & Kelly, 2007, p. 1087)—has attracted the attention of researchers in fields ranging from psychology to business to education (e.g., Robertson‐Kraft & Duckworth, 2014; Robins, 2019). Continuing the line of research that explores the domain specificity of grit (e.g., F. T. C. Schmidt, Fleckenstein, Retelsdorf, Eskreis‐Winkler, & Möller, 2017), this study introduces the L2‐Teacher Grit Scale (L2TGS) developed to measure grit specifically among English language teachers ( N = 202). The results demonstrated, first, that the L2TGS possessed sufficient internal‐consistency reliability (ω = .77). A subsequent principal components analysis revealed a two‐component structure (POV = 50.87%), thus yielding evidence in favor of construct validity. A one‐tailed Pearson’s test for positive correlation between Duckworth and Quinn’s (2009) domain‐general Grit–S and L2TGS scores established concurrent validity of the new measure ( r c = .84). Lastly, the L2TGS exhibited a stronger predictive validity, explaining approximately 21% of the variance in L2‐teacher retention‐related scores compared to the Grit–S, which was a statistically nonsignificant predictor accounting for 4% of the total variance. Of note, female teachers had higher levels of grit than male teachers. In sum, our findings indicate support for an occupation‐specific approach to grit.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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