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Record W3010202333 · doi:10.37546/jalttlt41.4-4

Does Dyslexia Occur Among Japanese English Language Learners?

2017· article· en· W3010202333 on OpenAlexaboutno aff
Elton LaClare

Bibliographic record

VenueThe Language Teacher · 2017
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsDyslexiaPsychologyReading (process)LiteracyAffect (linguistics)Association (psychology)OrthographyBiological theories of dyslexiaPopulationLinguisticsPhonological awarenessCognitive psychologyMedicineDevelopmental dyslexiaCommunicationPedagogy

Abstract

fetched live from OpenAlex

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。

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.015
GPT teacher head0.312
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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