Childhood bilingualism : research on infancy through school age
Bibliographic record
Abstract
Childhood Bilingualism Research: An Introduction Erika Hoff and Peggy McCardleProcessing Two Languages 1.Bilingual Speech Processing J. F. Werker, W. M. Weikum, and K. A. Yoshida (University of British Columbia) 2. When Infants Hear Two Languages: Interpreting Research on Early Speech Perception by Bilingual Children A. Fernald (Stanford University) 3. The Onset of Word Form Recognition in One Language and in Two M. M. Vihman (University of Wales, Bangor), J. A. G. Lum (University of Manchester), G. Thierry (Univ. of Wales, Bangor), S. Nakai (Univ. of Edinburgh), and T. Keren-Portnoy (University of Wales, Bangor)Speaking Two Languages 4. Bilingual First Language Acquisition in Perspective F. Genesee (McGill University) 5. Social Factors in Bilingual Development R. E. Eilers (University of Maine), B. Zurer Pearson (University of Massachusetts), A. B. Cobo-Lewis (University of Maine), Acquiring Literacy in Two Languages 6. Developing Literacy in English-Language Learners D. August (Centre for Applied Linguistics), M. Calderon (John Hopkins University) , Maria Carlo (University of Miami), and M. Nuttall (University of Houston) 7. Bilingualism at School E. Bialystok (York University)Perspectives on Childhood Bilingualism from Related Fields 8. Adult Bilingualism and Bilingual Development J. Kroll (The Pennsylvania State University.) 9. Finding the Points of Contact: Language Acquisition in Children Raised in Monolingual, Bilingual and Multilingual Environment S. Waxman (Northwestern University)Closing Comments 10. Multiple Perspectives on Research on Childhood Bilingualism M. Crago (McGill University) 11. An Agenda for Research on Childhood Bilingualism - Peggy McCardle and Erika Hoff
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".