An example of multilingual language use: Ukrainian and Russian loanwords in the Transcarpathian variety of the Hungarian language in Ukraine
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 article considers the peculiarities of the language use of Transcarpathian Hungarian students, with a focus on the knowledge of the meanings of Ukrainian and Russian loanwords. In this research, a total of 63 students from local colleges, universities and vocational schools solved five different tasks during an online survey. They were asked to describe pictures in Hungarian, to replace Ukrainian and Russian borrowings with Hungarian standard equivalents, to solve multiple choice questions on the meaning of loanwords, to name concepts based on definitions, and to list additional loanwords from their everyday language use. The results show that the majority of the Transcarpathian Hungarian students are familiar with the meanings of Slavic lexical borrowings; however, they prefer the Hungarian language equivalents due to their mother tongue dominance. The only exceptions are culture-specific terms (xenisms), including the names of currencies, institutions, and food.
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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 | 0.001 |
| 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 | 0.000 |
| 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