The representation of gender in the mind of Spanish-English bilinguals: Insights from code-switched Adjectival Predicates
Bibliographic record
Abstract
This study examines bilinguals’ gender use strategies in code-switched agreement (i.e. the moon is bonita) and concord (i.e. la moon) structures. Thirty-five L1 Spanish-L2 English adult bilinguals and 43 L1 English-L2 Spanish adults with an intermediate (N=18) or advanced (N=25) level of proficiency in Spanish completed an acceptability judgment task in which they rated code-switched Adjectival Predicates and DPs. The results show that only the L1 Spanish-L2 English bilinguals prefer the Adj (in the case of agreement) or the D (in the case of concord) to be marked for the gender of the Spanish translation equivalent of the English N, but that all groups rate agreement structures higher than concord structures. Both of these findings corroborate previous work on intrasentential code-switching, however, this is the first study to offer an account for the contrast in processing difficulty between agreement and concord structures. We argue that this difference can be explained in terms of the way in which the features are valued in agreement and in concord. Under the double-feature valuation mechanism (Liceras et al., 2008) in agreement both features are valued in a single direction, while in concord the features are valued in two different directions. It is this unidirectionality of the feature valuation mechanism in agreement that makes code-switched agreement structures such as Adjectival Predicates easier to process.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".