The Multilingual Language Program: formative evaluation of three implementation models in an early learning setting
Bibliographic record
Abstract
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.
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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 | 0.057 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".