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Record W2031446367 · doi:10.1075/jicb.2.1.02bur

Three factors in vocabulary acquisition in a university French immersion adjunct context

2014· article· en· W2031446367 on OpenAlexaffabout
Sandra Bürger, Alysse Weinberg

Bibliographic record

VenueJournal of Immersion and Content-Based Language Education · 2014
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAdjunctCLARITYVocabularyFrench immersionContext (archaeology)PsychologyClass (philosophy)Mathematics educationComputer scienceLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This study investigates the role of teaching, context, and repetition in the acquisition of specialized vocabulary. It involves thirteen students enrolled in a French immersion class linked to a French adjunct language course at the University of Ottawa, Canada. Based on Webb’s (2008) classification, the researchers have examined and rated the contexts in which students were exposed to a sample of thirty words in their immersion course (lectures and readings). Of these, some (n = 22) were taught explicitly over the semester and others (n = 8) were not taught, as they were words students encountered incidentally in their readings or lectures. Results in this study showed that: a) incidental exposure did not lead to vocabulary acquisition regardless of clarity of context and number of exposures, and b) explicit teaching led to differential learning outcomes not fully explained by clarity of context or number of exposures. The study concludes with a discussion of other factors affecting vocabulary learning in the immersion adjunct context.

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)0.010
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.016
GPT teacher head0.266
Teacher spread0.250 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Immersion and Content-Based Language EducationSame topicSecond Language Acquisition and LearningFrench-language works237,207