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Record W2470958900 · doi:10.1075/lab.15004.per

Catalan-Spanish bilingualism continuum

2016· article· en· W2470958900 on OpenAlexaff
Sílvia Perpiñán

Bibliographic record

VenueLinguistic Approaches to Bilingualism · 2016
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsCatalanNeuroscience of multilingualismGrammarLinguisticsPsychologyTask (project management)Philosophy

Abstract

fetched live from OpenAlex

Abstract This study investigates the expression of Catalan cliticsenandhi, which have no grammatical equivalent in Spanish, in the adult grammar of Catalan-Spanish early bilinguals. Participants (N = 57), born and raised in Catalonia, are divided into 3 groups according to their onset of acquisition and language use: Spanish-dominant (n = 20), Balanced Bilinguals (n = 15) and Catalan-dominant (n = 22). The results of an Acceptability Judgment Task and an Elicited Production Task indicated that Spanish-dominant bilinguals have a divergent grammar compared to that of the Catalan-dominant speakers, overaccepting ungrammatical omission and doubling of the clitics. The bilingual group patterned with the Catalan-dominant group in some of their judgments, but with the Spanish-dominant group in their production. It is argued that onset of acquisition cannot be the only explanation for the differences between the bilingual groups, and that quantity and quality of input play an important role in the acquisition process.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.138
GPT teacher head0.300
Teacher spread0.161 · 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

Citations40
Published2016
Admission routes1
Has abstractyes

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