Predicting individual differences in L2 speakers’ gestures
Bibliographic record
Abstract
While it is well known that there is a lot of variability in L2, researchers rarely measure the variability in L1 to predict the variability in L2. In this study we tested two explanations of the rate of gestures used in telling a story in L2: (1) a story-telling style underlying both L1 and L2 and (2) proficiency/fluency in L2. Hindi—English bilingual adults performed a story-telling task in their two languages. There was some support for the second predictor: the participants used more gestures in L2 than L1, consistent with gesturing to aid in accessing language. However, the results were strongly supportive of a story-telling style underlying both languages: the individual differences in gesture rate (along with the story length and the vocabulary variability) were highly correlated across languages but not correlated with L2 proficiency/fluency. These results illustrate the importance of studying L1 variability as an important predictor of L2 gesture use.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".