Labels or Concepts? The Development of Semantic Networks in Bilingual Two-Year-Olds
Bibliographic record
Abstract
The adult lexicon links concepts and labels with related meanings (e.g. dog–cat). How do children’s encounters with concepts versus labels contribute to semantic development? Three studies investigated semantic priming in 40 monolinguals and 32 bilinguals, who have similar experience with concepts, but different experience with labels (i.e. monolinguals hear “dog”, bilinguals hear “dog” and “chien”). Similarities in performance across monolinguals and bilinguals at age 24 months, as well as across bilinguals’ two languages at age 30 months, support the position that encounters with concepts contribute more to early semantic development than encounters with labels. Findings also suggest that the effects of semantic priming are challenging to observe at 24 months, but are strong in bilinguals by age 30 months.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".