Attending to form and meaning in processing second language input : a study of advanced second language learners
Bibliographic record
Abstract
This study replicates VanPatten's research (1990) in order to determine whether learners of Spanish as a second language (L2) can simultaneously attend to meaning and form when processing input. My research furthers VanPatten's work through an investigation of more advanced students of Spanish L2. The participants for this study were 60 advanced Spanish L2 students at McGill University in Montreal, Canada. To investigate whether advanced L2 learners can focus on form while listening for meaning, participants were randomly assigned to complete one of four listening tasks: Task I (control task): listening to the passage for content only, Task II: listening to the passage for content and simultaneously noting the key lexical item, inflacion, Task III: listening to the passage for content and simultaneously noting the article la, and Task IV: listening for content and simultaneously noting the verb morpheme -n. Comprehension of the passage was assessed by a written recall protocol. Results revealed that comprehension scores were higher among learners listening only for meaning than those of learners attending to meaning and one of the formal features. The findings of the present study are in agreement with VanPatten (1990). Learners' attention to form while listening for meaning appears to affect comprehension.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".