Norwegian L1 teachers’ beliefs about a multilingual approach in increasingly diverse classrooms
Bibliographic record
Abstract
The L1 subject is a central meeting place for all students regardless of their linguistic backgrounds. Thus explorations of multilingualism in the L1 subject provide the potential for enhancing all students’ multilinguality. In Norway, several policy papers have emphasised the important role of the L1 Norwegian subject in promoting students’ multilingualism as a resource, but guidelines are lacking on how this should be facilitated. Consequently, L1 teachers’ beliefs about multilingualism are central to understanding how and to what extent they implement a multilingual pedagogy. Whereas previous research in the Norwegian context has mainly explored foreign language teachers’ beliefs on multilingualism, this study investigated L1 Norwegian teachers’ beliefs on how the subject can be taught in an increasingly diverse language classroom. Ten upper secondary school teachers discussed these topics in focus groups. Most of the teachers had a language-as-problem orientation to their students’ multilingualism. In particular, they found it challenging to improve minority students’ Norwegian skills. Moreover, they rarely encouraged the use of minority students’ multilingualism as a resource in the classroom. They also reported little guidance from textbooks. The findings suggest a strong need for supporting L1 subject teachers in developing a multilingual mindset and for creating suitable teaching materials.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".