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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 OpenAlex
Yu-Cheng Lin, Pei-Ying Lin

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

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.

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)0.000
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 score0.629
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.005
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