Concepts of Borrowings in Modern Science of Linguistics, Reasons of Borrowed Words and Some of Their Theoretical Problems in General Linguistics
Bibliographic record
Abstract
In this article we study the problem of language contacts, the current state of some theories concerning the linguistic loans. For this purpose, the works of many scholars have been our basis for analysis; among them there are French and other European scientists, scholars of the post-Soviet period. We found that the contacts cause the mixture of languages despite the language borders. The various social events, nomadic life, campaigns and the military services, trade, cultural exchange and other factors favor linguistic loans. In this article we considered and studied different conceptions of matter “language contact”. We also approached, studying the opinions of scientists on the matter of linguistic borrowing of the structurally related and remote languages. The issue of bilingualism has been studied and studies allowed us to conclude about the different possibilities of the typology of bilingualism: a) the linguistic typology of bilingualism; b) the sociolinguistic typology of bilingualism; c) the typology psychological bilingualism. This article does not pretend to fully reflect all theories that exist on linguistic borrowing.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.006 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".