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Record W6906337305 · doi:10.17605/osf.io/zrynf

Adverb placement processing in English by balanced English-French bilinguals

2022· other· en· W6906337305 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGrammarAdverbPriming (agriculture)Rule-based machine translationSyntaxContrast (vision)Neuroscience of multilingualismLanguage acquisitionSecond-language acquisition

Abstract

fetched live from OpenAlex

Previous findings suggest a shared-syntax account of language representation of bilingual populations. Language acquisition and bilingual attainment are affected by a huge cluster of variables, among which the maturational factor and manner of acquisition (MoA) are prominent. The purpose of this study is to investigate how age of immersion (AoI) and MoA modulate the shared syntactic representation of (balanced) bilinguals. From a language production perspective, studies on cross-language syntactic priming have contributed to the shared-syntax hypothesis (Hartsuiker, 2021; Hartsuiker et al., 2004; Loebell & Bock, 2003; Muylle et al., 2021). For instance, the syntactic blocking hypothesis (SBH) predicts that if two languages are distinguishable in terms of two similar but non-identical structures (or word orders), cross-language priming between these two structures will be robust (Hartsuiker, 2021). Code-switching is another evidence of grammatical integration during production. Recently, a formal approach to bilingual grammar is proposed to account for the empirical data of code-switching under the framework of Minimalism Distributed Morphology (López, 2020). From a language comprehension perspective, some EEG studies on phrase-level processing have indicated that bilinguals tend to not inhibit ungrammatical structure in one language of theirs when it is grammatical in the other (e.g. Sanoudaki & Thierry, 2014). This is argued as evidence of co-activation of two grammars during processing. However, little research has been done to test the shared syntactic representation (of L1 and L2) at the sentence-comprehension level (but see Luque et al., 2018). The current study will explore this aspect by testing the grammatical co-activation of balanced English-French bilinguals while processing adverb placement in a self-regulated reading paradigm. In the literature on bilingual and L2 speakers there are controversial findings through grammatical competence tasks of investigating whether transfer or interference of adverb placement rules happens from one language to the other (Camacho & Kirova, 2018; White, 1990, 1991; Yuan, 2001). This could be due to the various language backgrounds of the participants in these studies. The Critical Period Hypothesis suggests that age of acquisition is the most critical factor for ultimate language attainment (Johnson & Newport, 1989, 1991; Lenneberg, 1967). In some particular environments where two languages are spoken at a similar dominant level, such as the Ottawa-Gatineau area in Canada, AoI and MoA have been suggested crucial for the organization of the mental lexicon of bilingual speakers (Sabourin et al., 2014). To investigate these issues, we ask three research questions: 1) Is there an integration effect during adverb placement processing for balanced English-French bilinguals? 2) How does AoI correlate with the integration effect? 3) Will MoA play a role in this process? To address these questions, we will collect some preliminary data from an online grammatical maze task (Witzel et al., 2012) which will be conducted to compare the processing patterns between two counterbalanced conditions (i.e., English vs. French word order of adverb placement) in English. Specific processing patterns will be elicited to investigate the correlation between the language background and the effect of syntactic integration.

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)0.006
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.008
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0120.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0530.001

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 head0.015
GPT teacher head0.324
Teacher spread0.309 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueOpen Science FrameworkFrench-language works237,207