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Incidental Learning of Vocabulary

2018· other· en· W2907350236 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

VenueThe TESOL Encyclopedia of English Language Teaching · 2018
Typeother
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVocabularyVocabulary learningActive listeningRepetition (rhetorical device)Reading (process)LinguisticsSalience (neuroscience)Computer sciencePsychologyVocabulary developmentArtificial intelligenceCommunication

Abstract

fetched live from OpenAlex

There are several factors that affect incidental learning of vocabulary, which may explain why second language (L2) learners pick up vocabulary at different rates. Some of these factors are: lexical difficulty level of texts, word repetition, generative word uses, contextual richness, and salience. Beginning L2 learners should focus on intentional vocabulary learning, intermediate learners on vocabulary learning strategies so that they can effectively learn from contexts, and advanced learners on incidental vocabulary learning from extensive reading. In order to acquire vocabulary incidentally from reading and listening, L2 learners need to understand at least 98% of the words in written texts and spoken language. Teachers can increase their students' rates of incidental vocabulary learning, by exposing them to written and spoken texts that are level‐appropriate, clear, rich, and supportive and that provide enough repetition of the new words.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1450.000

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 head0.006
GPT teacher head0.278
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