About eponyms in infocommunications and radio technologies terminology
Bibliographic record
Abstract
Eponimy problems in terminology of infocommunications and radio technologies are considered for the first time. Consideration of a linguistic aspect includes the analysis of 169 domestic publications in 27 directions of researches and allocation of medicine as a direction which concerns nearly half of all publications. It is supposed that the use of the term "eponym" became widely used by linguists in the last third or a quarter of the 20th century, as they adapted existing practices to assign personal names existing in medicine to a wide range of medical terms. Two types of dictionaries of the medical terms-eponyms differing in volume and structure are considered and analyzed. 64 eponyms-anthroponyms and one eponym-toponym are allocated from the standard programs of the speciality "Radio Engineering" (1984 version) as an example. The full name of the researcher, years of life, the country and disciplines using eponyms are established for eponyms-anthroponyms. It is established that the main "supplying countries" of eponyms-anthroponyms are the USA - 18, Great Britain - 11, France - 11, Germany - 10 and Russia - 6 eponyms. Drawn conclusions prove the need of continuation of researches in order to include: preparation of separate publications concerning forgotten or underexplored personalia, and preparation of the dictionary, which glossary has to correspond to modern programs of training of specialists. Results of such researches will promote formation of such competences as ability validly and delicately use historical heritage and ability to carry out the activity on the basis of complete system scientific outlook with use of knowledge in the field of history of science and equipment.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".