Gender Assignment and Agreement in L2 Spanish
Bibliographic record
Abstract
This study investigates gender assignment and agreement accuracy in the written productions of French-speaking learners of Spanish across three proficiency levels. Drawing on a medium-scale learner corpus, we coded all noun phrases for gender assignment (based on determiner inflection), for noun-adjective agreement, and for determiner-adjective agreement, and we examined the impact of various linguistic and learner-related predictors using Bayesian mixed-effects models. Although the overall error rate was relatively low, likely due to task type and familiar vocabulary, the models revealed robust effects of proficiency level and of underlying grammatical and lexical factors. Regarding gender assignment, accuracy was significantly lower for nouns with non-prototypical or ambiguous gender markers, for feminine nouns, and when Spanish and French differed in grammatical gender. Moreover, lower accuracy was observed with certain types of determiners. Noun-adjective agreement was influenced by the same factors, except for non-prototypical gender markings, which did not have a significant effect. In addition, less accuracy was observed with prenominal adjectives. Determiner–adjective agreement, in turn, only showed lower accuracy with feminine nouns, but the results of the statistical model should be interpreted with caution, due to high Pareto k values. Nevertheless, descriptive data confirm the relevance of distinguishing between noun–adjective and determiner–adjective agreement and highlight the need for larger corpora with a greater number of errors to model this phenomenon more conclusively. Overall, these findings contribute to a better understanding of gender processing in L2, demonstrate the value of medium-sized corpus analysis in second language acquisition research, and lay the groundwork for future research exploring crosslinguistic combinations beyond Spanish and French.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".