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Record W3173365077 · doi:10.1017/s0272263121000218

THE EFFECTS OF TALKER VARIABILITY AND FREQUENCY OF EXPOSURE ON THE ACQUISITION OF SPOKEN WORD KNOWLEDGE

2021· article· en· W3173365077 on OpenAlexaff
Takumi Uchihara, Stuart Webb, Kazuya Saito, Pavel Trofimovich

Bibliographic record

VenueStudies in Second Language Acquisition · 2021
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsConcordia UniversityWestern University
Fundersnot available
KeywordsStress (linguistics)PsychologyVocabularyRecallVowelLinguisticsWord (group theory)Variation (astronomy)Age of AcquisitionAudiologyCognitive psychologyCognition

Abstract

fetched live from OpenAlex

Abstract Eighty Japanese learners of English as a foreign language encountered 40 target words in one of four experimental conditions (three encounters, six encounters, three encounters with talker variability, and six encounters with talker variability). A picture-naming test was conducted three times (pretest, immediate posttest, and delayed posttest) and elicited speech samples were scored in terms of form-meaning connection (spoken form recall) and word stress accuracy (stress placement accuracy and vowel duration ratio). Results suggested that frequency of exposure consistently promoted the recall of spoken forms, whereas talker variability was more closely related to the enhancement of word stress accuracy. These findings shed light on how input quantity (frequency) and quality (variability) affect different stages of lexical development and provide implications for vocabulary teaching.

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)0.006
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.318
Teacher spread0.304 · 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

Citations28
Published2021
Admission routes1
Has abstractyes

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