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Record W3095311359 · doi:10.1075/bpa.10.03der

Issues for second language pronunciation in children

2020· book-chapter· en· W3095311359 on OpenAlexaff
Tracey M. Derwing

Bibliographic record

VenueBilingual processing and acquisition · 2020
Typebook-chapter
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsSimon Fraser UniversityUniversity of Alberta
Fundersnot available
KeywordsPronunciationPhonologyIntelligibility (philosophy)LinguisticsPsychologyCognitionSecond languageForeign languageFirst languageComputer scienceCognitive psychologyMathematics education

Abstract

fetched live from OpenAlex

Abstract Many L2 children exhibit native-like phonology, which can result in false assumptions about overall language development. For instance, teachers may assume that a child’s difficulties understanding schoolwork are due to cognitive delays rather than attributable to incomplete language acquisition. These suppositions can lead to the placement of L2 students in special education classes rather than language enrichment programming. Studies of children’s second language (L2) pronunciation development reveal that, contrary to popular opinion, some children have foreign accents. Although some L2 accents are easy to understand, requiring no intervention, pronunciation instruction research has identified strategies to enhance intelligibility when children’s productions are difficult to understand. Suggestions for assisting children and youth with intelligibility difficulties are made.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.344
Teacher spread0.318 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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