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Record W2146928064 · doi:10.1017/s030500091000022x

Mastering inflectional suffixes: a longitudinal study of beginning writers' spellings*

2010· article· en· W2146928064 on OpenAlexaff
Kathryn A Turnbull, S. Hélène Deacon, Elizabeth Kay‐Raining Bird

Bibliographic record

VenueJournal of Child Language · 2010
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsDalhousie UniversityWestern University
Fundersnot available
KeywordsMorphemeSpellingLinguisticsSuffixSpellPsychologyWord orderSociology

Abstract

fetched live from OpenAlex

This study tracked the order in which ten beginning spellers (M age=5 ; 05; SD=0·21 years) mastered the correct spellings of common inflectional suffixes in English. Spellings from children's journals from kindergarten and grade 1 were coded. An inflectional suffix was judged to be mastered when children spelled it accurately in 90 percent of the contexts in which it was grammatically required, a criterion used to study the order of acquisition of grammatical morphemes in oral language. The results indicated that the order in which children learned to spell inflectional suffixes correctly is similar to the order in which they learn to use them in oral language, before school age. Discrepancies between the order of mastery for inflectional suffixes in written and oral language are discussed in terms of English spelling conventions, which introduce variables into the spelling of inflected words that are not present in oral language.

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)0.002
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.321
Teacher spread0.305 · 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

Citations39
Published2010
Admission routes1
Has abstractyes

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