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Record W2088909735 · doi:10.5296/ijl.v6i3.5567

Acquisition of the Non-Generic Definite Article in English: The Influence of Cognitive Style

2014· article· en· W2088909735 on OpenAlexaboutno aff
Suzanne Prior, Keiko Fujise, Kimberley D. Fenwick

Bibliographic record

VenueInternational Journal of Linguistics · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive stylePsychologyIndependence (probability theory)Field dependenceStyle (visual arts)CognitionField (mathematics)ConventionTest (biology)Interpersonal communicationLinguisticsSocial psychologySociologyMathematicsSocial scienceStatisticsHistory

Abstract

fetched live from OpenAlex

The study examines the relationship between Japanese students’ uses of the English non-generic definite article and the cognitive style of field dependence/independence. According to a model by Liu and Gleason (2002), the non-generic definite article consists of four types: textual, structural, situation, and cultural. We examined whether the first three types, which involve analysis of grammatical rules, may be easier to learn for field independent learners who are more analytical. We also investigated whether cultural use, which is largely based on social convention, may be easier for field dependent individuals who have a more interpersonal orientation. Twenty-seven Japanese students studying in Canada completed a non-generic definite article test that involves filling in missing obligatory instances of the, the Group Embedded Figures Test that measures field dependence/independence, and four batteries of the Woodcock-Munoz Language Survey – Revised that together provide a measure of broad English ability. Textual and structural use were positively associated with a field independent style, over and above broad English ability. Other correlations were non-significant. Results are interpreted according to the type of cognitive learning required by the textual and structural uses of the and why these may be facilitated by a field independent orientation.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
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)0.0000.000

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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2014
Admission routes1
Has abstractyes

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