Russian as native, non-native, one of natives and foreign languages: Questions of terminology and measurement of levels of proficiency
Bibliographic record
Abstract
© 2015, Canadian Center of Science and Education. All rights reserved. The 21st century has brought globalization of people’s lives and education. Dramatic economical, political and natural cataclysms has made our planet’s population mobile and that concerns not only highly developed countries but also so called the third world countries. While moving and changing their places of residence people bring with them their native language, their culture, knowledge and experience. They also bring to their new county of residence their own perception about communication, both an inner communication and an intercultural one. Bulat Okudzhava said in one of his poems, “To understand each other is a sacred science”, and today this approach to communication becomes a vital necessity in everyday life, in the sphere of science and education, in real space as well as in the virtual one. While people actively learn foreign languages with approved status, minor languages become suppressed in spite of the fact that the population - bearers of these minor languages are quite numerous and these bearers should be taken into consideration. But this problem is likely to be referred to politics. In the frames of practical educational activities we deal with various problems. One of them quite often causes obstacles not only in organizing of the methodically correct educational process but also in its monitoring process. Its impact on marking the final results, on achieving targeted competences - all these are the subjects of correct terminology. To be more precise – correlation of terminology that is accepted in Russian Federation and in the world (in the first place in Europe, the USA, Israel).
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".