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Record W1968891843 · doi:10.3138/cmlr.68.2.164

The Effects of Sentence Writing on Second Language French and Korean Lexical Retention

2012· article· en· W1968891843 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Modern Language Review/ La Revue canadienne des langues vivantes · 2012
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsSentenceSecond languageWord (group theory)VocabularyPsychologyComputer scienceNatural language processing

Abstract

fetched live from OpenAlex

Abstract: This study investigates the effects of sentence writing (SW) on second language (L2) lexical retention by comparing two word-learning conditions: writing new words in sentences and repeated word-picture viewing. L2 learners of French and Korean attempted to learn 24 new words: 12 words with one condition and 12 words with another condition. Dependent measures were one immediate and two delayed posttests that required participants to produce target word forms. Results for both language groups revealed negative effects for SW, suggesting that SW can impede word-form learning during the initial stages of L2 vocabulary learning. Furthermore, the finding that the Korean learners' scores were much lower than the French learners' scores under the SW condition suggests that SW may result in even less retention when the L2 script is far more distant from one's first language (L1), thereby supporting the impact of L1–L2 orthographic distance on L2 word learning and retention.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0020.000

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 head0.011
GPT teacher head0.259
Teacher spread0.248 · 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