Multilingualism in Classroom Instruction: “I think it’s helping my brain grow”
Bibliographic record
Abstract
School systems in many countries typically view the home languages of multilingual students either as largely irrelevant or as an impediment to students’ educational progress. It is frequently assumed that because the teacher does not speak the multiple languages that may be represented in his or her classroom, there are no instructional options other than use of the national language (e.g., English) as the exclusive language of instruction. This normalized assumption is challenged in the present paper. Drawing on research carried out collaboratively with teachers across Canada over a 15-year period, I document ways in which students’ home languages can be incorporated into classroom instruction. This instructional approach, which I label ‘teaching through a multilingual lens’, is supported by an extensive range of research related to the effects of bi/multilingualism on students’ cognitive and metalinguistic development and the positive cross-lingual relationships between students’ first and second languages. The approach is also consistent with a philosophical and theoretical orientation that instruction should focus on teaching the whole child.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".