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Record W2030490892 · doi:10.1017/s0272263109990040

COMPREHENSION-BASED PRACTICE

2009· article· en· W2030490892 on OpenAlexaff
Pavel Trofimovich, Patsy M. Lightbown, Randall Halter, Hyojin Song

Bibliographic record

VenueStudies in Second Language Acquisition · 2009
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsComprehensionPsychologyLinguisticsComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

We report the results of a 2-year longitudinal comparison of grade 3 and grade 4 English-as-a-second-language learners in an experimental, comprehension-based program and those in a regular (i.e., more typical) language learning program. The goal was to examine the extent to which sustained, long-term comprehension practice in both listening and reading—in the virtual absence of any speaking—can help develop learners’ second language (L2) pronunciation. We analyzed learners’ sentences from an elicited imitation task using several accuracy and fluency measures as well as listener ratings of accentedness, comprehensibility, and fluency. We found no differences between the two programs at the end of year 1. However, at the end of year 2, there were some differences—namely, in the listener ratings of fluency and comprehensibility—that favored learners in the regular program. These findings highlight the beneficial effects of comprehension practice for the development of L2 pronunciation but also point to some potential limits of this practice.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.040
GPT teacher head0.435
Teacher spread0.396 · 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

Citations96
Published2009
Admission routes1
Has abstractyes

Explore more

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