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Record W2939064907 · doi:10.1017/s0142716419000110

Long-term outcomes for bilinguals in minority language contexts: Welsh–English teenagers’ performance on measures of grammatical gender and plural morphology in Welsh

2019· article· en· W2939064907 on OpenAlexaff
Hanna Binks, Enlli Thomas

Bibliographic record

VenueApplied Psycholinguistics · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsImpact
FundersEconomic and Social Research Council
KeywordsWelshPluralPsychologyContrast (vision)LinguisticsNeuroscience of multilingualismFirst languageDevelopmental psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This study explored the long-term effects of limited input on bilingual teenagers’ acquisition of complex morphology in Welsh. Study 1 assessed 168 12–13 and 16–17-year-old teenagers, across three bilingual groups: those whose first language was Welsh (L1 Welsh), those who learned Welsh and English simultaneously (L1 Welsh–English), and those who learned Welsh as a second language (L2 Welsh), on their receptive knowledge of grammatical gender. Study 2 assessed the same participants on their production of plural morphology. While the results of Study 1 revealed continuous progression toward adult norms among L1 Welsh-speaking bilinguals, with the simultaneous bilinguals progressing at a slower rate, the results of Study 2 revealed performances on plural morphology that were comparable to adult norms among the 16–17-year-old L1 Welsh-speaking bilinguals, and some progression among the simultaneous bilinguals. In contrast, delayed progression was seen among the L2 Welsh-speaking bilinguals across the board, with 16–17-year-old L2 participants lagging behind their L1 peers on both grammatical gender and plural morphology. The implications of these findings for our understanding of the long-term outcomes for bilinguals learning complex structures under minority language conditions are discussed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.038
GPT teacher head0.332
Teacher spread0.294 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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