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Record W4394721621 · doi:10.1017/s0142716424000043

How well do schoolchildren and adolescents know the form and meaning of different derivational suffixes? Evidence from a cross-sectional study

2024· article· en· W4394721621 on OpenAlexaff
Dalia Martinez, Danielle Colenbrander, Tomohiro Inoue, George K. Georgiou

Bibliographic record

VenueApplied Psycholinguistics · 2024
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyMeaning (existential)LinguisticsCross-sectional studyDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract As children advance through school, derived words become increasingly common in their reading materials. Previous studies have shown that children’s knowledge of derivational morphology develops relatively slowly, but there is more to learn about this development. This study examined differences in knowledge of the form and meaning of suffixes across grade levels (Grades 3, 5, and 8) and different types of derivational suffixes (adjectives and nominals). We assessed 309 English-speaking children on word reading and receptive vocabulary tests and two tasks designed to assess the form (orthographic knowledge) and meaning (semantic knowledge) of 28 derivational suffixes (14 adjectives and 14 nominals). Overall, our findings showed a significant improvement in identifying and understanding derivational suffixes from Grade 3 to Grade 5 and a smaller, but still significant, improvement from Grade 5 to Grade 8. Our findings regarding suffix types were mixed. While written forms of adjectives were identified more accurately than nominals across all grades, this advantage did not extend to the students’ understanding of the meaning of the suffixes. These results highlight the distinction between the identification of suffixes and the understanding of their meaning. We discuss our results in relation to suffix frequency in children’s reading materials.

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)0.000
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 score0.028
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.027
GPT teacher head0.329
Teacher spread0.302 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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