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Record W2150805776 · doi:10.1111/lang.12000

The Development of L2 Oral Language Skills in Two L1 Groups: A 7‐Year Study

2013· article· en· W2150805776 on OpenAlexaff
Tracey M. Derwing, Murray J. Munro

Bibliographic record

VenueLanguage Learning · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsSimon Fraser UniversityUniversity of Alberta
Fundersnot available
KeywordsPsychologyFluencyMandarin ChineseLinguisticsStress (linguistics)Slavic languagesFirst languagePronunciationSecond-language acquisitionWillingness to communicateLanguage proficiencySecond languageSecond-language attritionComprehension approachLanguage educationMathematics education

Abstract

fetched live from OpenAlex

Researching the longitudinal development of second language (L2) learners is essential to understanding influences on their success. This 7‐year study of oral skills in adult immigrant learners of English as a second language evaluated comprehensibility, fluency, and accentedness in first‐language (L1) Mandarin and Slavic language speakers. The primary data were judgments at three times from two sets of listeners: native monolingual speakers of English and highly proficient English L2 speakers. The Mandarin L1 speakers showed no change over time on any of the dimensions, while the Slavic language L1 speakers improved significantly in comprehensibility and fluency. Improvement in accent was limited to the first 2 years in the Slavic language group. These outcomes appear to be due to the complex interplay of L1, age, the depth and breadth of learners’ conversations in English, and their willingness to communicate.

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)0.005
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.279
Teacher spread0.267 · 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".

Quick stats

Citations256
Published2013
Admission routes1
Has abstractyes

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