Why<i>jumped</i>is so difficult: tense/aspect marking in Mandarin–English bilingual children
Bibliographic record
Abstract
Learning to mark for tense in a second language is notoriously difficult for speakers of a tenseless language like Chinese. In this study we test two reasons for these difficulties in Chinese-English sequential bilingual children: (1) morphophonological transfer (i.e., avoidance of complex codas), and (2) interpretation of -ed as an aspect marker of completion, like the Mandarin -le. Mandarin-English bilingual children and age-matched monolinguals did a cartoon retell task. The verbs used in the stories were coded for accuracy in English, telicity, and suppliance of -ed or -le. The results were consistent with morphophonological transfer: the bilingual children were more accurate with irregular past forms in English than regular forms. The results were also consistent with the bilingual children's interpretation of -ed as an aspect marker: most of their production of -ed was on telic verbs. We discuss possible reasons for the children's interpretation of -ed as an aspect marker.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".