National language in a globalised world: are L1 and L2 adolescents in Iceland more interested in learning English than Icelandic?
Bibliographic record
Abstract
Language acquisition and Language Maintenance (LM) both depend on ample opportunity and motivation. Currently, many national language societies are undergoing Language Shift (LS) to English, impacting their acquisition as first (L1) and possibly even more as second (L2) language. This study interviewed 44 adolescents, including 24 L1 and 20 L2 speakers of Icelandic, on their views on the importance of Icelandic and English, whether Icelandic is hard to learn, and their future plans. Qualitative and quantitative results showed highly similar views and future plans across groups; both languages were considered important but for different purposes. However, L1 speakers were far more likely to think that Icelandic is hard to learn, and only L1 speakers attached cultural value to Icelandic. L1 speakers were more ambivalent than L2 speakers on whether immigrants should learn Icelandic. L2 students’ ability to learn Icelandic appears impacted more by lack of opportunity than by lack of motivation. The study suggests that LS to English contributes to decreased opportunities to learn Icelandic as L2. It is further suggested that language policy in Iceland as well as international policy on which languages should be considered vulnerable due to a minority status need to be revisited in a globalised world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".