Gender Agreement in L3 Spanish Production among Speakers of Typologically Different Languages
Bibliographic record
Abstract
Grammatical gender presents persistent difficulty for adult learners of Spanish in L2 acquisition; however, there is a literature gap in L3 acquisition of gender, specifically of typologically different languages. In this project, we investigate the acquisition of Spanish gender agreement by Russian (L1)/Mandarin (L1)-English (L2) speakers of Spanish (L3) and compare the findings with English(L1) speakers of Spanish (L2). Studying these languages is particularly interesting because some exhibit an explicit gender system (Spanish and Russian) while others do not (English and Mandarin). In order to examine the effect of L1/L2 influence of these languages on L3 Spanish acquisition, 55 participants completed two tasks: a picture identification task and a grammaticality judgement task. Results indicate that advanced learners of Spanish of all L1 backgrounds performed at or near ceiling. All beginner learners performed better with canonically marked masculine nouns than noncanonical feminine nouns, thus corroborating previous findings. Regarding L1 influence, Russian participants outperformed the other two groups, especially in Task 1 (Picture Identification), thereby indicating that they may be transferring to some degree the grammatical gender system of their L1. Overall, this research provides evidence that multiple factors, including structural typology and L3 proficiency level, play a role in L3 acquisition.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".