Does incidental English impact the L1 and L2 acquisition of a micro-language? The language exposure and proficiency of Icelandic students with different home language backgrounds
Bibliographic record
Abstract
In a digital age, English increasingly competes for time and status with many smaller societal languages. This study examined the language exposure and proficiency of older school-age children learning Icelandic as L1 and L2 alongside incidental English. 122 students, age 10–16 years (65 with Icelandic as L1, 42 as L2, and 15 as one of two L1s) filled out self-reports of their oral and written language proficiency and a diary documenting current language use. Cumulative Icelandic exposure was obtained from parent report. 93 of the students were also administered a formal Icelandic test. All groups spent a similar time using English but in different settings. Individual variability was large. Cumulative Icelandic exposure predicted L2 Icelandic tested performance but not self-ratings. Current exposure to each language was generally associated with higher performance in that language. Current English use was associated with lower Icelandic performance. Studies of L2 learning need to be expanded to more diverse contexts than those comprising a strong societal and a heritage language. Iceland is an example of a context where clearer policy may be required to ensure that L2 speakers not only maintain their home language but also have adequate opportunity to master their societal language.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".