Slavic and East European Language Programs and Heritage Language Communities
Bibliographic record
Abstract
Among Slavic and East European heritage communities, the post-1989 geopolitical situation in Central and Eastern Europe has changed both emigration patterns and core aspects of the relationship between speakers in the homeland and abroad. Many speakers have both an enhanced motivation to maintain their heritage languages and greater resources to do so. As a reflection of this increased interest in Slavic and East European heritage languages, recent years have witnessed a rise in the number and scope of community language schools, established primarily by parents who wish to ensure that their children maintain active use of their heritage languages. At the same time, many Slavic and East European language programs at the college level have increasingly come under threat, due to the combination of reduced enrollments, greater administrative focus on class sizes, and a loss of federal funding. In this paper, using Czech as the base language, I suggest that by placing a greater emphasis on connections with heritage communities, we may be able to enhance the viability of Slavic and East European programs at the college level. This potential is supported by a marked increase in research on heritage language learners over the past two decades, which provides a foundation for curricular adjustments that address the specific needs of heritage language learners.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".