MétaCan
Menu
Back to cohort
Record W3081900102 · doi:10.1017/s0272263120000297

HOW DOES MODE OF INPUT AFFECT THE INCIDENTAL LEARNING OF COLLOCATIONS?

2020· article· en· W3081900102 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

VenueStudies in Second Language Acquisition · 2020
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsActive listeningReading (process)PsychologyVocabularyAffect (linguistics)Word learningLinguisticsIncidental learningVocabulary learningMode (computer interface)Cognitive psychologyMatching (statistics)CommunicationComputer science

Abstract

fetched live from OpenAlex

Abstract There has been little research investigating how mode of input affects incidental vocabulary learning, and no study examining how it affects the learning of multiword items. The aim of this study was to investigate incidental learning of L2 collocations in three different modes: reading, listening, and reading while listening. One hundred thirty-eight second-year college students learning EFL in Taiwan were randomly assigned to three experimental groups (reading, listening, reading while listening) and a no treatment control group. The experimental groups encountered 17 target collocations in the same graded reader. Learning was measured using two tests that involved matching the component words and recalling their meanings. The results indicated that the reading while listening condition was most effective while the reading and listening conditions contributed to similarly sized gains. The findings suggest that listening may play a more important role in learning collocations than single-word items.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.993

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.025
GPT teacher head0.349
Teacher spread0.324 · 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