Russian as a Heritage Language: From Research to Classroom Applications
Bibliographic record
Abstract
The goal of the proposed presentation is to advance the dialogue between the linguistically- and pedagogically-oriented traditions of heritage language research. Capitalizing on the work of multiple authors in the presenters’ edited collection published by Routledge in April, 2024, the paper will highlight how this mutually beneficial cross-pollination can lead to a deeper and more nuanced understanding of heritage bilingualism and improved classroom practices.\nThe talk will present new empirical findings on Russian as a HL that are based on a variety of frameworks and approaches, including research on HL speakers’ linguistic and pragmatic competence, literacy development, and sociocultural characteristics of Russian HL in the diaspora. Drawing on this research, the authors will then discuss specific examples of how these findings can inform pedagogical practices. For example, the talk will highlight implications of eye-tracking research on reading in Russian HL for acquiring reading skills by adult HL learners. Other examples include areas that HL practitioners often identify as central to their instructional needs: HL phonetics and reading, acquisition of pragmatics and register variation, and sociolinguistic studies of communities in which HL transmission takes place.\nThe authors hope to show that pedagogical implications should be drawn from a wide range of approaches spanning classroom-based, applied, and empirical linguistic studies and that any analysis of empirical findings on HLs must take into account their unique socio-political contexts: e.g., the volume which serves as the basis of this presentation investigates Russian as a HL in a variety of majority language contexts (U.S.A., Canada, Israel, Germany, Spain, Finland, Estonia, Sweden, and Cyprus) and within a variety of frameworks and approaches, spanning research on Russian HL speakers’ linguistic and pragmatic competence, literacy development, and sociocultural characteristics of Russian.\nWe hope that this approach based on dialogue on empirically- and pedagogically-driven research can serve as a model for other heritage languages in the future.\nKisselev, O., Laleko, O., & Dubinina, I. (2024). Russian as a Heritage Language: From Research to Classroom Applications. Routledge.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".