The Language Friendly School: supporting teachers as transformative agents of change
Bibliographic record
Abstract
This article highlights research collected during a year-long critical participatory ethnographic study at a primary school in Trinidad and Tobago. The study presents the experiences of two teacher collaborators who engage in the processes of problem identification, design and implementation of a language-friendly plan, reflective practice and knowledge mobilisation. Drawing inspiration from the Language Friendly School [Le Pichon-Vorstman & Kambel, 2021. Language-friendly pedagogy and children's well-being. https://hundred.org/en/articles/languagefriendly-pedagogy-and-children-s-well-being#1ec8c421, 2022], this research highlights the importance of collaborative dialogue in bottom-up approaches to language-inclusive education. Particularly, the research advocates for consequential validity [Cummins, 2021b. Evaluating theoretical constructs underlying plurilingual pedagogies: The role of teachers as knowledge-generators and agents of language policy. In E. Piccardo, A. Germain-Rutherford, & G. Lawrence (Eds.), The Routledge Handbook of plurilingual language education. Routledge] of the Language Friendly School, as an approach that is not only valid in theory but also extremely effective in practice. Considering the teachers' roles in this study, the findings highlight their important function as key stakeholders in inspiring sustainable change through educational practice. This article highlights the effectiveness of the language-friendly approach in creating space for teacher agency at the heart of bottom-up approaches to multilingual education.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.004 |
| 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".