Belonging, Conflict and Loss: Learning Ukrainian Online During COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper draws on findings from a duoethnography of two adult, heritage language learners of Ukrainian who began language learning online during pandemic restrictions. In duoethnography, researchers place life histories side by side with the goal of sharing multiple understandings of a social phenomenon. In this paper, we weave our life histories together to find meaning in how we are learning Ukrainian online during the pandemic and the ongoing Russian-Ukrainian conflict. The differences between us, including race and proximity to heritage language speakers, have affected our upbringing and development as people but they haven’t affected our emotional attachment to Ukrainian. We recognize this same attachment in the adult diasporic learners we meet through online classes. Ultimately, our narratives of belonging and acceptance in an online language learning community may have something to offer language education researchers and practitioners working with communities of diasporic heritage language learners.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it