Understanding Dyslographia (Chinese Dysgraphia) and What is Known About the Disorder
Bibliographic record
Abstract
Dysgraphia is a disorder in written expression that can be categorized into either developmental dysgraphia or acquired dysgraphia. There are three subtypes of developmental dysgraphia: dyslexic dysgraphia that bears similarities to dyslexia; dysgraphia due to motor clumsiness; and dysgraphia due to defect in understanding of space. The characteristics exhibited by these subtypes may apply to language systems that are alphabetical or phoneme-based in nature. For languages that are logographically based such as Chinese or Japanese, dysgraphia exists in another subtype known as dyslogographia. Though literature on dysgraphia is very limited as compared to studies done on dyslexia, literature on dyslogographia is even more limited. This paper will attempt to discuss about dyslogographia while drawing parallels if possible to the more known subtypes of dysgraphia and to some extent, also dyslexia.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".