Bibliographic record
Abstract
The paper aims to consider the flow of English loan words into present-day Georgian for the last twenty-five years after the country gained its independence as a result of the dissolution of the Soviet Union. Georgia ( Sakartvelo − in the Georgian language) is a small picturesque country with ancient culture located at the crossroads of Eastern Europe and Western Asia in the Caucasus region of Eurasia. The change of the country’s political orientation, as well as the democratisation of the society and its aspiration towards NATO and EU integration, have replaced the use of the Russian language by English due to the growth of American influence as well as the prestigious role of English as a lingua franca in almost every aspect of life at a global level. Therefore, in the present paper the term anglicism is used in its wide sense referring to English loans originating both from England and the USA. The research has shown that, like many European languages, present-day Georgian distinguishes three main groups of anglicisms that are differentiated from each other on the basis of the linguistic strategies of their borrowing: lexical, transliterated and semantic borrowings. The increasing flow of English words into Georgian confirms that the country and its people respond to the changing needs of communication, following changes in the world and ways of living in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".