Fostering community support for multilingual education: the language friendly approach
Bibliographic record
Abstract
For the past fifteen years, research on inclusive pedagogy has strongly advocated for schools to embrace the increasing linguistic diversity within their communities by adopting a multilingual, or “language friendly” approach. Multilingual approaches are linked to positive learning outcomes, but research has found resistance from communities towards embracing home languages for learning purposes. This is the first study to explore potential resistance towards the language friendly approach specifically, which differs from other approaches by offering flexibility in the way schools integrate home languages into classroom instruction. Qualitative data were used to understand to what extent, if any, schools faced resistance towards the approach. If they experienced resistance, how did they manage it? And importantly, how have community and network characteristics and school-based strategies helped mitigate resistance and foster support for the approach? The findings show that feelings of resistance were minimal and limited to teachers during the initial transition period. We tentatively conclude that the language friendly approach provides a support system that empowers teachers, students, and their families and naturally meshes with the values and multilingual realities of the school communities, limiting resistance.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".