MétaCan
Menu
Back to cohort
Record W2800714040 · doi:10.1075/itl.00007.web

Re-examining the effects of word writing on vocabulary learning

2018· article· en· W2800714040 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

VenueITL Review of Applied Linguistics · 2018
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsVocabularyWord (group theory)Task (project management)RecallWord learningTest (biology)Vocabulary learningPsychologyComputer scienceLinguisticsNatural language processingCognitive psychology

Abstract

fetched live from OpenAlex

Abstract This study investigated the effects of word writing on vocabulary learning by comparing three conditions in which there was (a) limited time to write words, (b) unlimited time to write words, and (c) a non-writing word-picture pairs comparison. Non-native speakers studying English as a second language encountered 8 word-picture pairs in each condition and were administered a test measuring form recall and another measuring receptive knowledge of written form. The results indicated that there was little difference between scores on both tests when time on task was the same. However, when the participants had as much time as they needed to write and learn words they had higher scores on both dependent measures than when learning in the other two conditions. The findings suggest that an ecologically valid approach to word writing may facilitate vocabulary learning.

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)0.005
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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)0.0040.000

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 head0.018
GPT teacher head0.327
Teacher spread0.310 · 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