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A Systems Framework of Bilingual Language Acquisition: How Development, Experience, and Contexts Interact to Shape Outcomes

2025· article· en· W4413751963 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Developmental Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsConcordia University
Fundersnot available
KeywordsPsychologyLinguisticsComputer scienceCognitive psychologyPhilosophy

Abstract

fetched live from OpenAlex

Millions of children grow up learning multiple languages, yet their outcomes vary dramatically: Some have high proficiency across languages while others have more limited abilities in some languages. This review presents a systems framework for understanding diverse trajectories in bilingual language acquisition. Drawing on systems theories, we examine how multiple levels of influence interact, from individual factors, such as maturational processes that lay the foundation, to immediate language experiences with family and educational contexts that provide learning opportunities. These experiences unfold both dynamically over time and within broader societal contexts that determine language status and community support. The framework reveals how successful bilingual development depends on alignment across system levels: Children, equipped with powerful learning abilities, must meet rich and sustained language experiences, as well as supportive social conditions. This approach illuminates systematic patterns in bilingual development and emphasizes coordinated, multilevel approaches for supporting bilingual development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.397
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it