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Record W1967687286 · doi:10.1177/0022219414529334

A Study of the Relationships Among Chinese Multicharacter Words, Subtypes of Readers, and Instructional Methods

2014· article· en· W1967687286 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Learning Disabilities · 2014
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyCharacter (mathematics)Reading (process)DyslexiaLinguisticsWord (group theory)Mathematics educationMathematics

Abstract

fetched live from OpenAlex

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.

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.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.347
Teacher spread0.308 · 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