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Record W3158588040 · doi:10.5281/zenodo.4449725

Multilingualism and third language acquisition

2021· article· en· W3158588040 on OpenAlexaboutno aff
Jorge Pinto, Nélia Alexandre

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistic Education and Pedagogy
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsMultilingualismLinguisticsGermanMetalinguistic awarenessLanguage acquisitionTurkishSecond-language acquisitionFirst languageContext (archaeology)PortugueseSociologyPedagogyHistory

Abstract

fetched live from OpenAlex

The purpose of this book is to present recent studies in the field of multilingualism and L3, bringing together contributions from an international group of specialists from Austria, Canada, Germany, Portugal, Spain, Switzerland, Turkey, and United States. The main focuses of the articles are three: language acquisition, language learning and teaching. A collection of theoretical and empirical articles from scholars of multilingualism and language acquisition makes the book a significant resource as the papers present a wide perspective from main theories to current issues, reflecting new trends in the field. The authors focus on the heterogeneity and complexity that characterize third language acquisition, multilingual learning and teaching. As the issues addressed in this book intersect, it represents an asset and therefore the texts will be of great relevance for the scientific community. Part I presents different topics of L3 acquisition, such as syntax, phonology, working memory and selective attention, and lexicon. Part II comprises texts that show how the research on language acquisition informs pedagogical issues. For instance, the role of the knowledge of previous languages in the teaching of L3, the attitudes of multilingual teachers to plurilingual approaches, and the benefits of crosslinguistic pedagogy versus classroom monolingual bias. In sequence, Part III consists of texts on individual learning strategies, such as motivation and attitudes, crosslinguistic awareness, and students’ perceptions about teachers’ “plurilingual nonnativism”. All these chapters include several different languages in contact in an acquisition/learning context: Basque, English, French, German, Italian, Ladin, Portuguese, Russian, Spanish, and Turkish.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.059
GPT teacher head0.291
Teacher spread0.233 · 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 designTheoretical or conceptual
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

Citations1
Published2021
Admission routes1
Has abstractyes

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