Professional development of heritage language teachers : the example of Lithuanian heritage language school in Iceland
Bibliographic record
Abstract
The aim of this paper is to explore literature related to teachers’ professional development in heritage language schools as units of non-formal education with a special focus on Lithuanian heritage language school in Iceland. \nDue to the limited opportunities of education and professional development the place of teachers of community-based heritage language schools among the teaching professions is not clear, though it is agreed that it differs from the teachers of formal educational settings. The need for heritage language education is widely recognized in most of the Western countries. Nevertheless, the implementation is often politicized, slow, challenging and highly dependent on state and local governments. The investigation of the situation in Canada and some European Nordic countries shows positive development. \nResearch on heritage language schools often is focused on teacher or student experiences or through ethnographic observation of the heritage language community schools. Lithuanian heritage language teaching is researched as a part of Lithuanian identity development or through structural description of school settings. However, heritage language teachers’ professional development is little researched both in Iceland and abroad. \nThe documentation and sharing of the information related to the quality of teaching, teacher professionalism and professional development could be improved in all heritage language schools of Lithuanian immigrant communities in different countries, including Lithuanian heritage language school in Iceland.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".