재미 한국어 계승어 학습자 작문에 나타난 통사 복잡성: Syntactic complexity in the writing of Korean heritage learners in the United States
Bibliographic record
Abstract
ABSTRACT Complexity, along with accuracy and fluency, is one of the dimensions that are primarily used to measure one's language proficiency level (Larsen-Freeman, 2006). As heritage language learners tend to speak the language in home and community environments only, they have relatively limited exposure to a more advanced, elaborate language. This study aims to investigate how Korean-American school-aged children use the Korean grammatical items related to syntactic complexity based on their writing samples to draw its implications on language education for Korean heritage learners. Using a revised form of the frequency analysis method (Ellis, 1994), 407 writing samples of heritage language learners at different proficiency levels in the United States and 40 samples of native Korean speakers in South Korea were analyzed. The results showed that the use of related grammatical items of heritage language learners dramatically changed over time, showing the dynamics of language development. However, compared to native Korean speakers, the range of grammatical items which frequently appeared in Korean-American heritage learners writing samples was distinctly limited to those mostly used in oral communication. Implications for Korean education as a heritage language are discussed.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".