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Record W2031748343 · doi:10.1017/s0142716412000173

Testing the statistical learning of spelling patterns by manipulating semantic and orthographic frequency

2012· article· en· W2031748343 on OpenAlexaff
S. Hélène Deacon, Dilys Hay Lok Leung

Bibliographic record

VenueApplied Psycholinguistics · 2012
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British ColumbiaDalhousie University
Fundersnot available
KeywordsMorphemeSpellingPsychologyAllomorphLinguisticsOrthographic projectionControl (management)Cognitive psychologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

ABSTRACT This study tested the diverging predictions of recent theories of children's learning of spelling regularities. We asked younger (Grades 1 and 2) and older (Grades 3 and 4) elementary school–aged children to choose the correct endings for words that varied in their morphological structure. We tested the impacts of semantic frequency by including three types of words ending in - er : derived and inflected forms, the first of which are far more frequent across the language, and one-morpheme control forms. Both younger and older children were more likely to choose the correct ending for derived forms over one-morpheme control words. This difference emerged for inflected forms only for the older children. We also tested the impacts of orthographic frequency by contrasting performance on the two derived allomorphs - er and - or , the first of which is far more frequent, and comparison one-morpheme forms. Both younger and older children were more likely to choose the correct spelling for the derivational - er over the same letter pattern in control words. This difference did not emerge in either group for the - or ending. The implications of these findings for current models of children's spelling development are discussed.

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 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 score0.102
Threshold uncertainty score0.727

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 studies0.0000.000
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 head0.037
GPT teacher head0.313
Teacher spread0.276 · 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 teacher head, 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

Citations11
Published2012
Admission routes1
Has abstractyes

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