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Record W2150757988 · doi:10.64152/10125/66646

How well does teacher talk support incidental vocabulary acquisition?

2010· article· en· W2150757988 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueReading in a Foreign Language · 2010
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsConcordia University
FundersConcordia University
KeywordsPsychologyVocabularyVocabulary developmentSecond-language acquisitionLanguage acquisitionLinguisticsTeaching methodMathematics education

Abstract

fetched live from OpenAlex

Opportunities for incidental vocabulary acquisition were explored in a 121,000-word corpus of teacher talk addressed to advanced adult learners of English as a second language (ESL) in a communicatively-oriented conversation class. In contrast to previous studies that relied on short excerpts, the corpus contained all of the teacher speech the learners were exposed to during a 9-week session. Lexical frequency profiling indicated that with knowledge of 4,000 frequent words, learners would be able to understand 98% of the tokens in the input. The speech contained hundreds of words likely to have been unfamiliar to the learners, but far fewer were recycled the numbers of times research shows are needed for lasting retention. The study concludes that attending to teacher speech is an inefficient method for acquiring knowledge of the many frequent words learners need to know, especially since many words used frequently in writing are unlikely to be encountered at all.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.1340.001

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 head0.006
GPT teacher head0.277
Teacher spread0.271 · 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