Building an elicited imitation task as a measure of implicit grammatical knowledge
Bibliographic record
Abstract
Elicited imitation tests (EIT) are often used to measure the effects of a form?focused intervention on the development of L2 learners’ implicit grammatical knowledge, which is intuitive and retrieved without conscious awareness. This study investigated the relationship between an EIT focused on the English past tense and a set of other tests; it also examined whether the past-tense morpheme type, grammaticality of the verbs, their position in the stimulus utterances, and the explicitness of the test instructions influence learners’ imitation accuracy. Forty-four university-level students, all native speakers of Mandarin and enrolled in an English for academic purposes program in Canada, completed an EIT along with four other tests over two consecutive sessions. Results revealed that only the imitation of irregular verbs was significantly correlated with the other tests. In addition, the participants were more accurate in repeating the grammatical and correcting the ungrammatical regular verbs but neither the position of the verbs nor the type of instructions significantly influenced imitation. These findings suggest that several factors can influence learners’ performance and the knowledge that they draw upon during an EIT.
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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 | 0.002 | 0.017 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".