“I Really Want to Save Our Language”: Facing the Challenge of Revitalising and Maintaining Southern Sami Language through Schooling
Bibliographic record
Abstract
This article is based on a study of Southern Sami language learning in Norway. There are around 600-1000 Southern Sami living widely dispersed over a large territorial area in Norway. As an indigenous people, they have a right to instruction in their own language. The Southern Sami language however is in danger of extinction. The purpose of this article is to explore how Southern Sami language learning is organised and implemented in school and, whether this training contributes to revitalising and maintaining the language. Data is collected in the contexts of the main Southern Sami language learning schools through qualitative interviews with pupils, teachers, headmasters, and parents. A sociocultural theoretical framework is used to analyse the data. The findings show that Southern Sami language learning in school offers very limited access to a Southern Sami language community due to the small number of pupils and teachers, lack of learning materials and most importantly the overall lack of language arenas for Southern Sami language. Another finding was that enthusiasm and motivation for learning and saving the language was very extensive.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".