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Record W4413879525 · doi:10.1515/mlt-2024-0007

The Multilingual Language Program: formative evaluation of three implementation models in an early learning setting

2025· article· en· W4413879525 on OpenAlexafffundabout
Catrine Demers, April MacDonald Killins, Andrea A. N. MacLeod

Bibliographic record

VenueMulticultural Learning and Teaching · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFormative assessmentLanguage acquisitionProgram evaluationMathematics educationComputer sciencePsychologyMultilingualismPedagogyLinguisticsPolitical science

Abstract

fetched live from OpenAlex

Abstract Multilingual families contend with the challenge of transmitting their minoritized language to their children and then supporting their children’s maintenance of this language. In this paper, we will present the Multilingual Language Program implemented with multilingual preschool-aged children in a Canadian urban context. The Program goals included to provide opportunities to hear and use minoritized languages in a preschool setting, to support the development of a bilingual identity, and to encourage all children to communicate. We will focus on a Formative Evaluation of three implementation models that took place over the course of one year with a partner organization working with preschool-aged children. The first model was hosted in-person in the partner’s preschool setting; the second model took place online with children participating with their parents at home; and the third model was a hybrid approach with an online delivery with children participating with their teachers in the classroom. The three models implemented required different adaptations and delivery, revealed different advantages and disadvantages regarding the collaboration with parents and support for the minoritized language at home, but were all valuable and provided multilingual language experiences to children. The evaluation of these models and their implementation will help inform future efforts of researchers and practitioners when designing a delivery model for their local context and participant needs.

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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.469
Teacher spread0.415 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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