Gender and number agreement in nonnative Spanish
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it