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Null and Overt Pronouns in Language Attrition

2019· reference-entry· en· W2982654260 on OpenAlexaff
Ayşe Gürel

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsLinguisticsPsychologyGrammarAttritionGenerative grammarLinguistic competenceNeuroscience of multilingualismFirst languageSecond-language acquisitionSubject pronounPhenomenonDevelopmental linguisticsPronounNatural languageMedicineComprehension approach

Abstract

fetched live from OpenAlex

This chapter provides an overview of studies on the use/interpretation of first language (L1) pronominals by late bilinguals who immigrated as adults to a second language (L2), living there for an extended period speaking the majority L2. It discusses how, among other linguistic properties, vulnerability in the L1 pronominal system has been documented. The chapter discusses why pronominals have become topical in L1 attrition research and reviews relevant research, demonstrating how different linguistic analyses proposed for adult L2 acquisition can also help identify the (un)changing characteristics of mature L1 grammar. The chapter deliberately confines itself to generative linguistics-based L1 attrition studies involving late bilinguals residing in an L2 country as first-generation immigrants, who typically become dominant L2 users after puberty, after which developmental point the L1 grammatical competence is believed to stabilize. Thus, any qualitative changes in L1 grammar of post-puberty bilinguals may have far-reaching implications for the alterability of L1 linguistic competence due to the L2. Studies discussed in this chapter are thus revealing as to the nature of the L1 attrition phenomenon in the pronominal domain.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.002
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 head0.019
GPT teacher head0.303
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2019
Admission routes1
Has abstractyes

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Same topicLanguage Development and DisordersFrench-language works237,207