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Record W2026675477 · doi:10.1080/17586801.2014.943645

Exploring the effects of word features on French immersion children's ability to deconstruct morphologically complex words

2014· article· en· W2026675477 on OpenAlexaff
Kathleen Hipfner-Boucher, Katie Lam, Xi Chen, S. Hélène Deacon

Bibliographic record

VenueWriting Systems Research · 2014
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsDalhousie UniversityInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsCognateMorphemeLinguisticsPsychologyFrenchWord lists by frequencyWord (group theory)Second languageTask (project management)French immersionPhilosophy

Abstract

fetched live from OpenAlex

The present study investigated factors influencing the ability to decompose multimorphemic words in French in non-francophone children educated in French. In particular, we focused on the effects of two word features: English-French cognate status and base frequency. We also examined the effect of child language background (English first language (EL1) versus English second language (ELL)) on performance. In two related studies, children in grades 1 to 3 completed a translation task requiring them to match morphologically complex words in French and English. Target words were manipulated with respect to cognate status and base frequency. Overall, performance was found to improve over time and to be influenced by cognate status and word frequency. Across all grades, EL1 and ELL children were comparable on task performance. Taken together, these results suggest that French immersion students' ability to deconstruct words and extract morphemes in French is influenced by the presence of cognates, as well as base frequency.

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)0.005
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 score0.547
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.101
GPT teacher head0.377
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

Citations4
Published2014
Admission routes1
Has abstractyes

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