Fostering community support for multilingual education: the language friendly approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it