Bilingual lexical selection as a dynamic process: Evidence from Arabic-French bilinguals.
Bibliographic record
Abstract
The nature of the lexical selection process in bilingual spoken word production is one of the pending questions of research on bilingualism. According to one view this competitive process is language-specific, while another holds that it is language-nonspecific (i.e., lexical competition is cross-linguistic). In recent years, research on bilingual language production has seen the rise of a third view that postulates that lexical selection is in fact dynamic and may function as language-specific or nonspecific depending on a number of factors. The aim of the present study was to investigate the lexical selection process among moderately proficient bilinguals whose two languages are typologically distant: Tunisian Arabic and French. The picture-word interference task was used in two experiments where moderately proficient Tunisian Arabic (L1)-French (L2) bilinguals were asked to name pictures in their L2 while ignoring auditory distractors (semantic, phono-translation, phonological, or unrelated) in their L2 (Experiment 1) or their L1 (Experiment 2). Thus, the language context was entirely monolingual in Experiment 1 and bilingual in Experiment 2. In Experiment 1, only a phonological facilitation effect was observed. In Experiment 2, interference was found in the phono-translation, semantic, and phonological conditions. Taken together, these results indicate that cross-language competition occurs among moderately proficient Tunisian Arabic-French bilinguals only in a bilingual context (Experiment 2) as indexed by the phono-translation interference effect observed. Our findings are in line with the recent hypothesis that lexical selection is a dynamic process modulated by factors like language similarity, language proficiency, and the experimental language context.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".