LANGUAGE APTITUDE AND GRAMMATICAL DIFFICULTY
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.
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 the passive (a difficult structure) and the past 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 the passive but not the past progressive on the written measure. Another component of aptitude, associative memory, contributed to learners’ gains on the past progressive on 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).
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it