Cross-Linguistic Effects of Bilingualism
Bibliographic record
Abstract
For bilinguals, the use and knowledge of one language affects how they process the other. Various cross-linguistic influences (CLI) can be observed in both language production and comprehension across all domains of linguistics. We start by broadly exploring the concept of transfer, both negative and positive, and forward and reverse. In doing so, we identify various classifications of CLI. Following this, we review key studies on phonological, lexical, morphological, and syntactic transfer along with other types such as discursive, pragmatic, and sociolinguistic. We find that there are a number of factors that can determine the degree to which transfer emerges or whether it happens at all. We then review studies investigating the ability to switch between the two language systems. In doing so, we look at theoretical models that explain what facilitates language switching and what empirical studies tell us about the neural and electrophysiological activity that arises in language switching. Finally, we discuss dreaming and bilingualism and argue that dreaming in an L2, contrary to popular belief, does not necessarily imply fluency in that language.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".