Awareness of L1/L2 differences: does it matter?
Bibliographic record
Abstract
This study is an investigation of the extent to which francophone learners of English as a second language (ESL) are aware of the differences between French and English question formation and how such awareness relates to their L2 performance. Three tasks were administered to 58 grades 5 and 6 francophone ESL learners. In a grammaticality judgement task, learners were asked to judge the grammaticality of English Wh– and yes/no questions. In a scrambled questions task, participants were instructed to create questions with sets of words written on individual cards. Some of the participants were also interviewed. Students’ own grammaticality judgement and scrambled questions tasks were used as stimuli for the interviews. On the grammaticality judgement task, questions in which the subject was a pronoun were judged more accurately than questions in which the subject was a noun. The most frequent non-target question forms that learners produced on the scrambled questions task were those in which a word (e.g. auxiliary do) was ‘fronted’ (placed at the beginning of a declarative sentence). The interview indicated that most students had a poor understanding of differences between English and French questions. Correlation analyses showed a positive relationship between students’ awareness of L1–L2 differences and their ability to correctly judge and form questions in English.
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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 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".