MétaCan
Menu
Back to cohort
Record W2888488504 · doi:10.1111/1467-9817.12256

Establishing word representations through reading and spelling: comparing degree of orthographic learning

2018· article· en· W2888488504 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Research in Reading · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsSaint Mary's University
FundersMedical Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsSpellingReading (process)Orthographic projectionPsychologyLinguisticsOrthographyWord (group theory)Word recognitionNatural language processingComputer scienceCognitive psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Skilled reading involves rapid and automatic word recognition. Through a self‐teaching process, phonological decoding during reading is thought to establish the word‐specific representations in memory that support efficient word reading. Much is known about orthographic learning during reading; less is understood about this process during spelling. Here, we compared the degree of orthographic learning that occurs during reading and spelling. Forty‐eight children in Grade 2 practised reading or spelling nonwords within stories. Orthographic learning was measured using spelling recognition, spelling production and word naming tasks. Both readers and spellers showed evidence of orthographic learning; however, spellers outperformed readers on all tasks. Overall, results suggest that spelling sets up a higher quality representation in memory and highlight the importance of spelling in the development of word reading efficiency.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.055
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.235
GPT teacher head0.473
Teacher spread0.238 · 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