Total Immersion or Bilingual Education? Findings of International Research on Promoting Immigrant Children’s Achievement in the Primary School
Bibliographic record
Abstract
Population mobility is at an all-time high in human history. The movement of people from one country to another has resulted in a significant increase in linguistic diversity in urban schools in countries around the world. This increase in diversity has given rise to numerous issues related to educational policies and classroom pedagogy for immigrant students. For example: What patterns of educational achievement do immigrant children show in school? How long does it take immigrant students to catch up to native speakers of the school language in both conversational and academic language skills? What role does students’ home language (L1) play in their learning of the school language (L2)? Should immigrant students be totally immersed in the school language or is there a role for bilingual education, which uses students’ L1 as a medium of instruction for part of the school day? The present paper focuses on the latter two questions. However, in order to understand the potential roles of L1 promotion for immigrant students, the general patterns of immigrant student achievement will be briefly reviewed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".