The role of immersion learning in the acquisition and processing of L2 gender agreement
Bibliographic record
Abstract
Abstract In this paper, we examine the effects of learning environment on second language (L2) gender agreement. English speakers learning L2 Spanish participated in a self-paced reading task and a picture selection task prior to and after a short-term study abroad experience. The results from the self-paced reading task showed that their reliance on the masculine article as the default (e.g., McCarthy, Corrine. 2008. Morphological variability in the comprehension of agreement: An argument for representation over computation. Second Language Research 24(4). 459–486) was reduced over time abroad. Findings from the picture selection task showed that the learners did not attend to the gender of articles unless it was their only cue, but that after the study abroad experience they began to use gender as an anticipatory cue for lexical selection. We interpret these results as support for an adapted version of the Shallow Structures Hypothesis (Clahsen, Harald & Claudia Felser. 2006a. Grammatical processing in language learners. Applied Psycholinguistics 27(1). 3–42; Clahsen, Harald & Claudia Felser. 2006b. How native-like is non-native language processing? Trends in Cognitive Sciences 10(12). 564–570) and the notion that in immersion contexts L2 learners shift their parsing strategy to be more communicatively focused (Schwieter, John W. & Gabrielle Klassen. 2016. Linguistic advances and learning strategies in a short-term study abroad experience. Study Abroad Research in Second Language Acquisition and International Education 1(2). 217–247).
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".