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Record W2886409123 · doi:10.1080/10888438.2018.1499745

Morphological Processing Before and During Children’s Spelling

2018· article· en· W2886409123 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

VenueScientific Studies of Reading · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsDalhousie University
FundersCoventry University
KeywordsMorphemeSpellingDictationLinguisticsHandwritingPsychologyOrthographyComputer scienceLexiconControl (management)DysgraphiaNatural language processingArtificial intelligenceReading (process)Speech recognitionDyslexia

Abstract

fetched live from OpenAlex

Our understanding of spelling development has largely been gleaned from analysis of children’s accuracy at spelling words under varying conditions and the nature of their errors. Here, we consider whether handwriting durations can inform us about the time course with which children use morphological information to produce accurate spellings of root morphemes. Six- to 7-year-old (n = 23) and 8- to 11-year-old (n = 25) children produced 28 target spellings in a spelling-to-dictation task. Target words were matched quadruplets of base, control, inflected, and derived words beginning with the same letters (e.g., rock, rocket, rocking, rocky). Both groups of children showed evidence of morphological processing as they prepared their spelling; writing onset latencies were shorter for two-morpheme words than control words. The findings are consistent with statistical learning theories of spelling development and theories of lexical quality that include a role of morphology.

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)0.001
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 score0.270
Threshold uncertainty score0.502

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 studies0.0010.001
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 head0.034
GPT teacher head0.337
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