TENDENCIES OF CREATING ANGLOPHONE ERGONYMS IN MODERN ENGLISH-SPEAKING WORLD
Bibliographic record
Abstract
The article is dedicated to clearing out the tendencies in creating Anglophone ergonyms in modern English-Speaking world."Onym" as a word, word combination or a sentence which serves for identifying a certain object among other objects, its individualization and identification, is under the author's consideration.Ergonyms as the names of companies, banks, factories, plants, educational establishments, cultural objects and sport events in Great Britain, the United States of America, Canada, Australia and New Zealand are researched in this paper in order to describe their structure, functions and influence on the people in the above mentioned countries.The research findings show that in all these countries most ergonyms are word combinations of toponimical or anthroponimical origin.Toponimical ergonyms denote the place where the object is located and anthroponimical ergonyms are connected with the names of personalities whose names they bare.The research proves that in Australia and New Zealand ergonyms are closely connected with the British English.People in these countries traditionally call their companies, banks, factories, plants, educational establishments, cultural objects and sport events with English names.Very few proper names reflect the languages of aboriginals or native population.Tendencies of creating Anglophone ergonyms are represented by the usage of Latin and Greek roots in toponimical ergonyms, borrowings from mass media, reflection of the national identification, traditional giving names of outstanding personalities who served their people to remarkable objects, banks, universities, memorials, theatres, libraries.These are so called anthroponimical ergonyms.In the future ergonyms to nominate different events, companies or cultural objects will be synchronized with the development of science, politics, IT computer sphere.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".