Should Schools Undermine or Sustain Multilingualism? an Analysis of Theory, Research, and Pedagogical Practice
Bibliographic record
Abstract
Summary Most school systems around the world prioritize the teaching of languages and aim to develop bilingual or multilingual proficiencies among their students. However, in a large number of contexts, schools also systematically and intentionally undermine the potential of immigrant-background and minoritized students to develop multilingual abilities. This undermining of multilingualism operates either by explicitly prohibiting students from using their home languages (L1) within the school or through ignoring the languages that students bring to school (benign neglect). In some cases, exclusion of students’ L1 is rationalized on the grounds that maintenance of L1 will hinder students’ integration into the mainstream society. In other cases, exclusion is based on the conviction that there is competition between languages and use of the L1 either in school or home will reduce students’ exposure to the school language (L2). The validity of this time-on-task argument is critically analyzed in the present paper. I argue that the research shows no consistent relationship between immigrant students’ academic achievement (in L2) and use of L1 in the home or in the school. By contrast, several research syntheses have highlighted the positive academic outcomes of bilingual programs for minoritized students and also the feasibility of implementing multilingual or translanguaging pedagogies in the mainstream classroom.
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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 | 0.041 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".