Gender and number agreement in nonnative Spanish
Bibliographic record
Abstract
This paper reports on an experiment investigating the acquisition of Spanish, a language that has a gender feature for nouns and gender agreement for determiners and adjectives, by speakers of a first language (L1) that also has gender (French), as well as an L1 that does not (English). Number (present in all three languages) is also investigated. Subjects were adult learners of Spanish, at three levels of proficiency, as well as a control group of native speakers. Oral production data were elicited. Subjects were also tested on an interpretation task, in which the selection of pictures corresponding to particular sentences depends on number and gender contrasts. The results from both tasks show significant effects for proficiency; low proficiency groups differ significantly from native speakers, but advanced and intermediate groups do not. There were no significant effects for L1 or for prior exposure to another second language with gender. The findings are discussed in the context of two different theories as to the possibility of parameter resetting in nonnative acquisition, namely, the failed functional features hypothesis and the full transfer full access hypothesis. The results are consistent with the latter hypothesis.
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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 | 0.006 |
| 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 | 0.000 |
| 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".