Bibliographic record
Abstract
Abstract The widespread occurrence of nouns in one language with a determiner in the other, often referred to as mixed NPs, has generated much theorizing and debate. Since both a syntactic account based on abstract features of the determiner and an account highlighting the notion of a Matrix language yield largely the same predictions, we assess how the tenets of each play out in speaker choices. The data derive from a massive corpus of spontaneous nominal mixes, produced by bilinguals in New Mexico, where bidirectional code-switching is the norm. Bilinguals’ choices concern (1) NP status (mixed vs. unmixed); (2) mixing type (limited-item vs. multi-word); and (3) language of the noun (here, English vs. Spanish). Results show that the community preference is for mixed NPs, independent of their theoretical felicity as dictated by determiner language properties. As to mixing type, these NPs are mostly constituted of lone nouns, such that the language of the determiner and any associated verb is perforce that of the surrounding discourse. Finally, the overwhelming choice is for English lone nouns incorporated into Spanish, and hence for a Spanish determiner. The language of the determiner thus proceeds, not from abstract linguistic properties, but instead from straightforward adherence to bilingual speech community conventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".