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Das zweisprachige Gehirn: Gehirnprozesse während des Spracherwerbs

2020· article· de· W3106646747 on OpenAlexfundno aff
Rafaela Bepe Gabriotti, Rosângela Zomignan

Bibliographic record

VenueRevista Científica Multidisciplinar Núcleo do Conhecimento · 2020
Typearticle
Languagede
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignUniversity of WashingtonVrije Universiteit BrusselBộ Giáo dục và Ðào tạoUniversity of CambridgeMcGill University
KeywordsGynecologyPolitical sciencePhilosophyMedicine

Abstract

fetched live from OpenAlex

Diese Arbeit ist eine Studie über Spracherwerb, Gehirnprozesse, die während des Erwerbs und Zweisprachigkeit beteiligt sind. Ziel dieser Forschung ist es, besser zu verstehen, wie zwei Sprachen gleichzeitig lernen, damit wir besser darauf vorbereitet sind, Kindern beim Spracherwerb zu helfen, sowie den Lehrer und die Familie durch theoretische Grundlagen zu unterstützen. Aspekte wie die kortikale Organisation der Sprache, Unterschiede zwischen dem zweisprachigen Gehirn im Vergleich zum einsprachigen Gehirn und der Einfluss der sozialen Interaktion auf das sprachliche Lernen werden in dieser Arbeit erläutert, um einen breiten Überblick über den zweisprachigen Spracherwerb zu bieten. Für diese Studie haben wir uns für die bibliographische Forschung ausländischer Literatur entschieden, da im Portugiesischen nicht genügend Materialien gefunden wurden, die die betrachteten Studienbereiche abdeckten. Die Ergebnisse zeigen, wie das Gehirn den Spracherwerb verarbeitet, zeigt den Unterschied zwischen dem gleichzeitigen und sequenziellen Erlernen zweier Sprachen und zeigt, wie soziale Faktoren und Sprache verbunden sind.

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)0.007
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0030.003
Scholarly communication0.0080.006
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0240.004

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 head0.046
GPT teacher head0.344
Teacher spread0.298 · 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 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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