Developmental Sentence Scoring for Japanese
Bibliographic record
Abstract
This article reports on the development and use of the Developmental Sentence Scoring for Japanese (DSSJ), a new morpho-syntactical measure for Japanese constructed after the model of Lee’s English Developmental Sentence Scoring model. Using this measure, the authors calculated DSSJ scores for 84 children divided into six age groups between 2;8 and 5;2 on the basis of 100-sentence samples collected from free-play child–adult conversations. The analysis showed a high correlation of the DSSJ overall score with the Mean Length of Utterance. The analysis of the DSSJ sub-area scores revealed large variations between these sub-area scores for children with similar overall DSSJ scores. When investigating the high-scoring children (over 1 SD over group average), most children scored high in three to five sub-areas, but the combination of scores for these sub-areas varied from child to child. It is concluded that DSSJ is a valuable tool especially for language acquisition research. The overall DSSJ score reliably reflects the overall morpho-syntactic development of Japanese children, and the sub-area scores provide specific information on individual acquisition patterns.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".