HL Mandarin Speakers Toss the Same Way as Fluent Mandarin Speakers
Bibliographic record
Abstract
Abstract Heritage language (HL) speakers often show weaker semantics in HL words than speakers who continue to learn and use the language. In this study, we tested whether HL Mandarin speakers simplified near-synonyms of throw verbs by diminishing the difference between the near-synonyms and/or by diminishing the difference between the generic throw verb and other near-synonyms. Two participant groups, HL Mandarin speakers and English second-language learners, acted out six Mandarin near-synonyms of throw verbs and the English verb throw. The results showed more similarities than differences between the two groups in the core features of throw verb semantics (force, speed, and direction). We observed few signs of simplification. One interpretation of these results is that early and/or naturalistic exposure to Mandarin was an important predictor of speakers’ knowledge of conceptual features. These results add to the literature showing that there can be factors beyond proficiency that contribute to speakers’ lexical semantics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".