Bibliographic record
Abstract
This study investigates the relationship between foreign language aptitude and the learning of two English structures defined as easy or difficult to learn. Using a quasiexperimental design, 66 secondary-level learners of English as a foreign language from three intact classes were provided with four hours of instruction on thepassive(a difficult structure) and thepast progressive(an easy structure). Language aptitude was measured using the LLAMA Aptitude Test (Meara, 2005). Language outcomes were measured with a written grammaticality judgment and an oral production task. The results revealed that one of the aptitude components, grammatical inferencing, contributed to learners’ gains on thepassivebut not thepast progressiveon the written measure. Another component of aptitude, associative memory, contributed to learners’ gains on thepast progressiveon the oral measure. The results provide support for the claim that different components of aptitude contribute to the learning of difficult and easy L2 structures in different ways. There is also support for the proposal that different components of aptitude may be involved at different stages of language acquisition (Skehan, 2002).
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".