A Study of the Relationships Among Chinese Multicharacter Words, Subtypes of Readers, and Instructional Methods
Bibliographic record
Abstract
This article reports the results of two studies examining the effectiveness of the whole-word and analytic instructional methods in teaching different subtypes of readers (students with normal reading performance, surface dyslexics, phonological dyslexics, and both dyslexic patterns) and four kinds of Chinese two-character words (two regular [RR], two irregular [II], one regular, one irregular [RI], and one irregular, one regular [IR]). The approaches employed were the analytic method, which focuses on highlighting the phonological components of words, and the whole-word method, which focuses on learning by sight. Two studies were conducted among a sample of 40 primary school students with different reading patterns. The aim was to examine the relationships among different subtypes of readers, two-character words, and instructional methods. In general, students with a surface dyslexic pattern benefited more from the analytic methods. Regarding combinations of different kinds of two-character words, all subtypes of students performed better in reading RR words than in reading II words.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".