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Record W2009708143 · doi:10.1017/s1366728911000071

Measuring metasyntactic ability among heritage language children

2011· article· en· W2009708143 on OpenAlexaff
Daphnée Simard, Véronique Fortier, Denis Foucambert

Bibliographic record

VenueBilingualism Language and Cognition · 2011
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPortugueseHeritage languageTask (project management)ComprehensionReading comprehensionRepetition (rhetorical device)LinguisticsPsychologyReading (process)Language proficiencyComputer scienceCognitive psychologyMathematics educationPedagogy

Abstract

fetched live from OpenAlex

Metasyntactic Ability (MSA) refers to the conscious reflection about syntactic aspects of language and the deliberate control of these aspects (Gombert, 1992). It appears from previous studies that heritage-language learners tend to demonstrate lower MSA than their monolingual counterparts (Lesaux & Siegel, 2003). In the present study, we verified whether the same results would be obtained among Portuguese heritage children living in a French-speaking environment when their MSA is measured using two different tasks. The participants were 22 Portuguese heritage children and 22 French monolingual elementary school children (mean age = 10.9 years). Five measurement instruments were used: a reading comprehension task; a language proficiency task; two metasyntactic tasks: a replication task in which the children had to identify and reproduce an error, and a repetition task, in which they had to repeat sentences containing syntactic errors; and a sociodemographic questionnaire. The results showed that when reading comprehension and language proficiency were controlled for, no effect of language background could be observed. However, reading comprehension and language proficiency differently influenced performances on MSA tasks.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.999

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)0.0010.000

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 head0.035
GPT teacher head0.282
Teacher spread0.246 · 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.

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

Citations14
Published2011
Admission routes1
Has abstractyes

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