Bibliographic record
Abstract
Dyslexia is the most commonly diagnosed learning disability in the English-speaking world, affecting between 10 and 20 percent of the adult population of countries such as the United States, Britain, and Canada (International Dyslexia Association, 2016). While diagnosis and treatment of dyslexia focusses on the act of reading, the underlying cause of the condition is thought to be a phonological processing disorder that inhibits an individual’s ability to identify separate speech sounds (International Dyslexia Association, 2002). Awareness of dyslexia has risen steadily among speakers of other languages, but for Japanese citizens and educators it remains a relatively unknown phenomenon. Differences in the orthography of languages affect reading in ways that can greatly impact the likelihood of an individual acquiring literacy (Paulesu et al., 2001). Understanding these influences is essential to ensuring the best outcomes for Japanese English language learners. 失読症は英語圏では一般的な学習障害であり、米国、英国、カナダなどでは成人人口の約10～20%に見られる(International Dyslexia Association, 2016;.)。失読症の診断や治療は読書する行為に着目しているが、その症状の原因は、個々の言語音(speech sounds)を聞き分ける能力を妨げる音韻（音素）処理障害と考えられている(International Dyslexia Association, 2002)。他 の言語の国々においても失読症への認識は高まっているが、日本の一般人や教育者においては未だ比較的認知されていない状態である。言語の正字法における違いは流暢に読めるようになる能力に大きく影響する(Paulesu et al., 2001)。これらの違いを理解する事は、全ての日本人英語学習者にとって最善の学習成果をもたらすために重要なものとなる。
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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 | 0.000 | 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".