When Gestures <i>Do</i> or <i>Do Not</i> Follow Language‐Specific Patterns of Motion Expression in Speech: Evidence from Chinese, English and Turkish
Bibliographic record
Abstract
Speakers of different languages (e.g., English vs. Turkish) show a binary split in how they package and order components of a motion event in speech and co-speech gesture but not in silent gesture. In this study, we focused on Mandarin Chinese, a language that does not follow the binary split in its expression of motion in speech, and asked whether adult Chinese speakers would follow the language-specific speech patterns in co-speech but not silent gesture, thus showing a pattern akin to Turkish and English adult speakers in their description of animated motion events. Our results provided evidence for this pattern, with Chinese-as well as English and Turkish-speakers following language-specific patterns in speech and co-speech gesture but not in silent gesture. Our results provide support for the "thinking-for-speaking" account, namely that language influences thought only during online, but not offline, production of speech.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".