Bilinguals process incoming words using distributions across both languages
Bibliographic record
Abstract
How does the bilingual experience affect online processing? The distribution of lexical items shared between monolinguals and bilinguals can differ greatly. One critical difference is how code-switching allows more variability in the relative co-occurrence of words. The current study uses a visual world paradigm to test whether the relative distribution between Spanish gender-marked determiners ("el," "la") and the non-marked English determiner ("the") predict the Spanish-English bilingual's ability to predict and/or integrate an incoming noun. While we replicate a previously observed asymmetry among Spanish-English bilinguals between the masculine "el" and feminine "la," our cluster permutation test results reveal differences in how bilinguals predict and integrate nouns when preceded by "el" versus "la" or "the." Comparing our results to existing corpus data, we argue that bilinguals rely on the distributional norms they experience across both single-language and code-switched contexts to facilitate online processing.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".