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Record W2553084387 · doi:10.1080/00220973.2016.1252999

The Beautiful and the Ugly: Reading Ability Modulates Word Spacing Effects in Chinese Children

2016· article· en· W2553084387 on OpenAlexaff
Yu-Cheng Lin, Pei-Ying Lin

Bibliographic record

VenueThe Journal of Experimental Education · 2016
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Saskatchewan
FundersShanghai Ocean UniversityNational Cheng Kung University
KeywordsReading (process)Mandarin ChineseWord (group theory)Contrast (vision)SalientPsychologyLinguisticsComputer scienceCognitive psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

There are no salient word spaces in Mandarin Chinese. Thus, it is unclear whether word spacing information differentially affects the reading speed of children with and without reading difficulties (RD). In the present study, native Chinese-speaking children of differential reading abilities were tested with Chinese text in un-spaced versus spaced versions at different time points during training. The results indicated that spaced texts slow down reading speeds in children without RD. In contrast, spaced texts improved reading speeds in children with reading difficulties after some training took place. These findings suggest that the effect of word spacing information on Chinese reading might vary as a function of individual differences in reading abilities. We argue that children with RD can accommodate to the spaced text better than children without RD and that they can take advantage of using bottom-up spacing information to segment and recognize words in text.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.310
Teacher spread0.304 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2016
Admission routes1
Has abstractyes

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