Acquisition of the Non-Generic Definite Article in English: The Influence of Cognitive Style
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".