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Record W2789905082 · doi:10.1017/s1366728918000214

Nonconvergence on the native speaker grammar: Defining L2 success

2018· article· en· W2789905082 on OpenAlexaff
Lydia White

Bibliographic record

VenueBilingualism Language and Cognition · 2018
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologySecond-language acquisitionPerspective (graphical)Language acquisitionGrammarMillerDevelopmental psychologyContrast (vision)Cognitive psychologyLinguisticsMathematics education

Abstract

fetched live from OpenAlex

The issue of critical or sensitive periods affecting the outcome of second language (L2) acquisition has been the subject of intense investigation and debate for many years, with people arguing for or against maturational effects on ultimate attainment. In their influential paper, Johnson and Newport (1989) identify two hypotheses: the exercise hypothesis and the maturational state hypothesis . According to the former, if the capacity for acquiring language is exercised early in life (in first language acquisition), then language learning abilities will remain intact throughout life: in other words, permitting successful L2 acquisition regardless of age. In contrast, according to the maturational state hypothesis, the language learning capacity declines with age, affecting L2 acquisition as well as late L1. Johnson and Newport take their results, which show an age-related decline in performance during childhood and adolescence, to support the maturational state hypothesis. Many L2 researchers have adopted a maturational perspective and have reached similar conclusions as to the presence of critical or sensitive periods (e.g., Abrahamsson, 2012; DeKeyser & Larson-Hall, 2005; Long, 1990; Oyama, 1976; Patkowski, 1980). Some researchers have pointed out that age effects continue into adulthood, contrary to the claim for a critical period (e.g., Birdsong & Mollis, 2001). Others have suggested that what looks like an age-related maturational decline may be accounted for by confounding factors, such as task effects, effects of L1, amount of L2 use, education or input (e.g., Bialystok & Miller, 1999; Flege, Yeni-Komshian & Liu, 1999).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.034
GPT teacher head0.347
Teacher spread0.313 · 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 designQualitative
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

Citations5
Published2018
Admission routes1
Has abstractyes

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