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Record W4377139345 · doi:10.5539/ijps.v15n2p41

The Difference between Monolingual and Bilingual Stutterers: A Systematic Review

2023· review· en· W4377139345 on OpenAlexvenueno aff
Zixuan Qi

Bibliographic record

VenueInternational Journal of Psychological Studies · 2023
Typereview
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyStutteringCognitionInhibitory controlExecutive functionsControl (management)Cognitive psychologyDevelopmental psychologyNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

The article is a comprehensive review regarding group differences of brain structures, cognitive process, and emotional responses between monolingual people who stutter (MWS) and bilingual people who stutter (BWS). Until now, researchers have been examining MWS and BWS separately, yet the compare between these two groups of people is lacking. In this work, the overview of MWS and BWS are introduced, different types and traits of stutter between MWS and BWS are compared, and the differences between brain structure, executive force, motor control, and cognitive reserve are investigated and synthesized based on previous studies. Both MWS and BWS have executive deficits in speech motor control, while BWS seem to be classified as having greater potential in language inhibitory and switching, thus transferring to general executive control. The conclusions are that BWS generally experience increased neural connectivity due to larger volume of grey and white matter than MWS, and thus gaining cognitive control abilities. Further research is warranted for the bilingual advantage and the exact etiology for stuttering.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.813
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.443
GPT teacher head0.603
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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