How bilinguals refer to Mandarin throwing actions in English
Bibliographic record
Abstract
Aims and Objectives: In the present study, we tested how Mandarin-English bilinguals choose English words to refer to prototypical Mandarin throwing actions. Languages differ in how they refer to events. In Mandarin and English, words for throwing actions differ notably on a variety of dimensions so there are few perfect translation equivalents. In previous studies, when faced with the challenge of how to speak about such events, bilinguals sometimes use language-specific ways in each language, sometimes show convergence, sometimes use more general terms, and there are times when they can be quite creative. Design/Methodology: We showed video clips of six prototypical Mandarin throwing actions (corresponding to rēng 扔, diū 丢, pāo 抛, tóu 投, shuāi 摔, shuǎi 甩) to Mandarin-English bilinguals and English monolinguals. Participants labeled the actions and chose the English word most closely corresponding to the action. The bilinguals did the same in Mandarin. Findings/Conclusion: The results showed that the bilinguals chose many of the same words in English as English monolinguals did. However, the bilinguals differed from the monolinguals in two ways: (1) they tended to choose more different responses and (2) they referred to diū 丢 actions most often as throw rather than lob as the monolinguals did. Originality: These results suggest that bilinguals use a variety of strategies to refer to the not-easily-translatable.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".