MétaCan
Menu
Back to cohort
Record W2144425798 · doi:10.1017/s0272263114000606

HOW DOES PRIOR WORD KNOWLEDGE AFFECT VOCABULARY LEARNING PROGRESS IN AN EXTENSIVE READING PROGRAM?

2015· article· en· W2144425798 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 · 2015
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsVocabularyReading (process)Affect (linguistics)PsychologyVocabulary learningVocabulary developmentSet (abstract data type)Term (time)Extensive readingTest (biology)Word (group theory)Foreign languageLinguisticsMathematics educationComputer scienceCommunication

Abstract

fetched live from OpenAlex

Sixty English as a foreign language learners were divided into high-, intermediate-, and low-level groups based on their scores on pretests of target vocabulary and Vocabulary Levels Test scores. The participants read 10 Level 1 and 10 Level 2 graded readers over 37 weeks during two terms. Two sets of 100 target words were chosen from each set of graded readers and were tested on three occasions. The results showed that the relative gains from pretest to immediate posttest were 63.18%, 44.64%, and 28.12% for the high-, intermediate-, and low-level groups, respectively. There was little decay in knowledge on the Term 1 three-month delayed posttest; relative gains ranged from 21.05% for the low-level group to 59.01% for the high-level group. The learning gains in Term 2 were consistent with those from Term 1. The results indicate that prior vocabulary knowledge may have a large impact on the amount of vocabulary learning made through extensive reading.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.397
Teacher spread0.356 · 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