Gender agreement among Russian learners of Spanish in an instructed versus naturalistic learning environment
Bibliographic record
Abstract
Abstract The present study examines Spanish gender agreement among beginner and non-beginner naturalistic and instructed native Russian learners of L3 Spanish. The project has two goals: first, to investigate whether the above groups differed in their target production and comprehension of gender agreement according to a series of morphological variables (gender class, type, and congruency) and secondly, to determine whether there was a relationship between accuracy and task completion times. A total of 49 native speakers of Russian learning Spanish as an L3, divided across two learner groups (24 instructed in Canada and 25 naturalistic in Mexico) and two proficiency levels (28 beginners and 21 non-beginners), along with a control group of 15 native Spanish speakers, completed several tasks. Results demonstrate that regardless of learning environment, native-like proficiency for gender agreement can be achieved at advanced levels. Differences were observed at the beginner level with the naturalistic group performing better with more difficult forms (e.g., feminine, non-canonical, and incongruent), indicating that at initial stages there is an advantage of naturalistic acquisition. Naturalistic learners had faster task completion times, though this did not correspond to higher accuracy levels. This study has important implications for the field of applied linguistics as it places importance on assessing gender acquisition across distinct learning environments.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".