Word Learning Seasons: Climate Effects on Early Vocabulary in Monolingual and Bilingual Children
Bibliographic record
Abstract
Children’s vocabularies reflect the dynamic worlds they inhabit, yet traditional models of language acquisition treat environmental influences as static. This study reveals how seasonal variations in weather systematically influence early vocabulary composition. Three studies involving 16–30 months old monolinguals and bilinguals (total N = 1294), examine how climate conditions affect word learning using parent-reported vocabulary checklists. Children in cold-weather regions (Wisconsin, Montreal) produced significantly more winter-related words compared to peers in milder climates (Texas, Florida), while seasonal analyses showed that within populations, winter word acquisition and knowledge peaked in the coldest months and declined during the warmest months. This pattern was most pronounced in older children, and in bilinguals with greater language exposure. Our research shows how children’s language systems adapt to environmental variations, advancing our understanding of language acquisition as a responsive process shaped by continuous interactions between cognitive development, language exposure, and the changing environmental landscape children navigate.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".