Do societal and individual multilingualism lead to positive perceptions of multilingualism and language learning? A comparative study with Australian and German pre-service teachers
Bibliographic record
Abstract
Many metropolitan cities have undergone rapid demographic changes in recent years, and such changes hasten and widen linguistic diversities. Similar changes are happening in Sydney, Australia and Hamburg, Germany. These changes are most acutely felt and observed in the classrooms where multiple languages are spoken, despite a prevalent monolingual mindset in education in both these contexts. What do pre-service teachers think of language learning and multilingualism in the face of demographic and sociolinguistic changes? This is a particularly urgent question for pre-service teachers, whose perspectives on multilingualism will considerably influence on how their students view language learning and maintenance. Based on a survey of 436 pre-service teachers in Sydney and Hamburg, this comparative study explores the relationship between their linguistic profiles (monolingual, multilingual and how they become multilinguals) and the way they perceive societal multilingualism and the need to promote multilingual education to all. The findings suggest that formal language education, more than heritage backgrounds and knowledge, provided the necessary experience to foster a more open attitude towards societal multilingualism and language learning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".