Bibliographic record
Abstract
The paper illustrates the decline of a minority language through the example of a village of an ethnic minority in Hungary.This process accelerates assimilation despite considerable support from the state.The language of any peoples or nationality in a minority position is endangered by the policies of the majority state, psychological pressure resulting from this, a voluntary surrender of the vernacular as a consequence, as well as mixed marriages.In case of Hungarian minorities outside the borders of Hungary, the stigmatisation of dialects also adds up to the picture, furthering the shift to the language of the majority.The sunny side of life as a member of minority includes the benefits of bilingualism and biculturalism, as for Transcarpatia: multilingualism and -culuralism.In case of a mutual, equal bi-or multilingualism the negative influence of one language to another is little.The Bashkir and Tatar languages from Russia are a good example for the bi-and multilingualism's positive influence.Until recent times these languages were used in education only for some subjects (native language and literature) in the first years of elementary school.In Bashkiria, Tatarstan and Yakutia, despite the constituion of the federation, the educational and language laws, children could learn in their native language till graduation.The results: the intellectual ration in the mentioned above territories grew one and a half times bigger than in the federation's Russian population.In some states, such as Serbia, Croatia, Slovenia and Austria the situation of Hungarian minority can be said to be favorable.In other states -including some EU members -the Hungarian identity is rather disadvantaged even though many examples can be found for positive minority and language politics, such as the situation in Finland, Switzerland and Canada.In closing, I'd like to encourage everyone to protect their native language, native dialect, because most of us have dialets as native language and there is nothing to be ashamed of in it.Let's strive to prevails the sunny side of multilingualism and multiculturalism.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".