Activation and local inhibition in the bilingual child’s processing of codeswitching
Bibliographic record
Abstract
Codeswitching has been used as a tool to investigate how the properties of the two language systems interact in the bilingual mind with relatively few studies investigating bilingual children. We target two groups of L1-Spanish–L2-English children in Spain to address language activation and language inhibition in the processing of codeswitching between a determiner (DET) and a noun (N). We investigate how the mental representation of the formal features involved is responsible for the sensitivity to grammatical gender, which in turn affects how bilinguals’ language activation and inhibition processes are at play and shape processing. We target both the directionality of the switch (English-DET–Spanish-N vs. Spanish-DET–English-N) and the type of implicit gender agreement mechanism (in the case of Spanish-DET–English-N switches) by using offline acceptability judgment data and eyetracking during reading data. Results suggest lower processing costs of English DET switches and higher ones of non-congruent Spanish DET switches. We interpret the preference for classifying the non-gendered Ns along the lines of the gendered Ns in the gendered language as evidence for the integrated representation hypothesis which states that both Ns depicting the same concept are connected in the mind of the bilingual.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".