Language-specific event role mappings in multimodal possession-transfer event descriptions
Bibliographic record
Abstract
Event descriptions require mapping event roles from an underlying conceptual representation to surface speech and gesture. Encodings in co-speech gesture tend to align with language-specific options that govern encodings in speech, but are relatively understudied for event roles that can be omitted in speech (e.g., argument-dropping languages like Turkish allow omission of core event roles, including agents and recipients). We examine the content of multimodal possession-transfer event descriptions across two typologically distinct languages (English, Turkish), differing in the grammaticality of argument-dropping. We find that language-specific encoding patterns heavily affect recipient and agent mentions in free event descriptions across modalities. Overall, Turkish speakers mentioned recipients and agents less frequently than English speakers. Although recipient and agent co-speech gestures were used more frequently in Turkish, they rarely contributed information beyond what was encoded in speech. This suggests that argument-dropping in Turkish occurs at a level of representation that is shared across modalities.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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; both teacher heads agree on what is shown here.
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".