Language Mixing in the Nominal Phrase: Implications of a Distributed Morphology Perspective
Bibliographic record
Abstract
This paper investigates a pattern found in Spanish–English mixed language corpora whereby it is common to switch from a Spanish determiner to an English noun (e.g., la house, ‘the house’), but rare to switch from an English determiner to a Spanish noun (e.g., the casa, ‘the house’). Unlike previous theoretical accounts of this asymmetry, that which is proposed here follows assumptions of the Distributed Morphology (DM) framework, specifically those regarding the relationship between grammatical gender and nominal declension class in Spanish. Crucially, and again in contrast to previous accounts, it is demonstrated that this approach predicts no such asymmetry for French–English. This hypothesis is tested experimentally using an acceptability judgment task with self-paced reading, and as expected, no evidence is found for an asymmetry. This experiment is also used to test predictions regarding how English nominal roots in mixed nominal phrases are assigned grammatical gender, and the impact of language background factors such as age of acquisition. Evidence is found that bilinguals attempt to assign analogical gender if possible, but that late sequential bilinguals have a stronger preference for this option than do simultaneous bilinguals.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".