Exploring the mediating role of emotions expressed in L2 written languaging in ESL learner text revisions
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
Abstract Little is known about the relationship between second language use and written languaging (e.g., Ishikawa & Suzuki, 2016 ; Suzuki, 2012 ). The few studies that have investigated the question highlight difficulties in understanding how to correct errors following written corrective feedback (e.g., Simard, Guénette, & Bergeron, 2015 ; Suzuki, 2012 ). Following the broaden-and-build theory, ( Fredrickson, 2001 ), we hypothesized that emotions expressed in the written languaging of high school ESL learners ( n = 42) produced immediately after receiving their corrected text would be related to text revision successfulness (i.e., correct revision of an incorrect form). Our results show that emotions expressed in the written languaging are not only associated with error rates but also positive emotions predict higher rates of successful revision.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 it