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Record W2901271358 · doi:10.1111/modl.12661

Incidental Vocabulary Learning Through Listening to Teacher Talk

2020· article· en· W2901271358 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueModern Language Journal · 2020
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsActive listeningVocabularyPsychologyVocabulary learningContext (archaeology)RecallMeaning (existential)Vocabulary developmentIncidental learningTest (biology)LinguisticsReading (process)Word (group theory)Cognitive psychologyTeaching methodMathematics educationCommunication

Abstract

fetched live from OpenAlex

Abstract This study investigated incidental learning of single‐word items and collocations through listening to teacher talk. Although there are several studies that have investigated incidental vocabulary learning through listening, no intervention studies have explicitly investigated the extent to which listening to teachers in a classroom context might contribute to vocabulary learning. The present study fills this gap. Additionally, the study explored the relationship between vocabulary learning gains and two factors: frequency of occurrence and first language (L1) translation. A meaning‐recall test and a multiple‐choice test were used to evaluate learning gains. The results indicated that (a) listening to teacher talk has potential to contribute to vocabulary learning of both single‐word items and collocations, (b) using L1 translation to explain target word meanings contributed to larger gains on the immediate posttest, (c) frequency of occurrence was not a significant predictor of incidental vocabulary learning.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1230.001

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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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