Cross-linguistic influence in the bilingual lexicon: Evidence for ubiquitous facilitation and context-dependent interference effects on lexical processing
Bibliographic record
Abstract
Abstract For bilinguals, lexical access in one language may affect, or be affected by, activation of words in another language. Research to date suggests seemingly contradictory effects of such cross-linguistic influence (CLI): in some cases CLI facilitates lexical access while in others it is a hindrance. Here we provide a comprehensive review of CLI effects drawn from multiple disciplines and paradigms. We describe the contexts within which CLI gives rise to facilitation and interference and suggest that these two general effects arise from separate mechanisms that are not mutually exclusive. Moreover, we argue that facilitation is ubiquitous, occurring in virtually all instances of CLI, while interference is not always present and depends on levels of cross-language lexical competition. We discuss three critical factors – language context, direction, and modality of CLI – which appear to modulate facilitation and interference. Overall, we hope to provide a general framework for investigating CLI in future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".