Initial Teacher Education and the Emotional Geography of Languages: A conceptual intervention
Bibliographic record
Abstract
The article addresses a key challenge faced by Initial Teacher Education (ITE) programs: how to reconcile the growing multilingual reality of society with the limited adoption of multilingualism in educational practice. It begins by providing an overview of ITE and some of its challenges. It then examines the importance of Critical Multilingual Language Awareness (CMLA), which emphasizes multilingualism as essential for equity and inclusion in linguistically diverse contexts. To extend the discussion of CMLA, the idea of Emotional Geography of Languages (EGL) is introduced as a conceptual framework grounded in the affective turn in Applied Linguistics and TESOL, the spatial turn in education, and Indigenous views of land-people relationality. EGL explores how emotions and identities, tied to places and languages, shape human relationships while challenging policies that marginalize mother tongues and heritage languages. The article concludes by demonstrating how EGL can inform teacher candidates’ CMLA, preparing them to contribute to pedagogical and social transformation in linguistically diverse settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".