Navigating Variability: Language Development in Linguistically Diverse Environments
Bibliographic record
Abstract
Over half of the world’s population is bilingual, meaning that most children grow up hearing multiple languages and/or accents. How do children navigate this input variability when acquiring their language(s)? In this thesis, I report three studies exploring this question in one of the most culturally and linguistically diverse regions in the world – the Greater Toronto Area. In Chapter 2, I report a large-scale study examining vocabulary development in children from a variety of language learning contexts. I replicate past findings reporting faster vocabulary development in monolingual children than bilingual children, while also revealing for the first time that exposure to substantial non-native input has no impact on vocabulary growth in monolingual children. In Chapter 3, I ask how linguistic experience shape toddlers’ speech processing. My findings demonstrate that monolinguals excel at processing the locally dominant variety of English, whereas bilinguals excel at adapting to unfamiliar varieties of English. And finally, in Chapter 4, I investigate how school-aged children process familiar and unfamiliar varieties of their native language. I found that monolingual and multilingual 4- to 6-year-olds process the locally dominant English variety similarly, but the larger English vocabularies of monolingual children gave them an edge over multilinguals in comprehending unfamiliar accent varieties. Together, these findings advance our understanding of how children in linguistically diverse environments handle speech variation in their input and suggest that they adapt by developing strategies that are fine-tuned to their own particular linguistic environment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".