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Record W2521752476 · doi:10.5539/ijel.v6n5p12

Animacy, Frequency and Working Memory Effects in the Acquisition of a Noun-Adjective Agreement Pattern in L2 under Incidental Learning Conditions

2016· article· en· W2521752476 on OpenAlexvenueno aff
Nadiia Denhovska

Bibliographic record

VenueInternational Journal of English Linguistics · 2016
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAnimacyAdjectivePsychologyNounSecurity tokenCognitionLanguage acquisitionCognitive psychologyLinguisticsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

<p>Animacy is recognized as an important feature in cognition and language processing. The present paper reports the results of an experiment that investigated the effects of animacy of the head noun (animate, denoting animals-epicenes, and inanimate, denoting non-living objects) and working memory on the learning of a noun-adjective agreement pattern in Russian. Participants were 60 novice learners whose L1 did not mark grammatical gender. The between-subjects design manipulated token frequency (high vs. low) under incidental learning conditions. No animacy effect was found in either learning condition. Working memory was a significant factor in both incidental learning conditions, and it explained a greater amount of variance in the high token frequency condition where accuracy was also significantly higher than in the low token condition. The results have implications for incidental learning research and language learning practices, specifically how different factors contribute to the acquisition of L2 grammatical knowledge.</p>

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 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)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
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)0.0010.000

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 head0.013
GPT teacher head0.313
Teacher spread0.300 · 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 teacher head, not a consensus.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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