Morphology and Lexicology Interface. Latest Russian Neologisms: The Next Step towards Analytism?
Bibliographic record
Abstract
Introduction This chapter examines Russian neologisms (new words) related to modern technology, the Internet and other forms of media, looks at their orthographic representations and discusses their effect on the Russian morphology, morphosyntax and the tendency towards increased analytism. The sample of Russian neologisms is taken from two major sources: neologism dictionaries and Internet texts. The dictionary entries in the study come from neologism dictionaries published at the end of the twentieth and the beginning of the twenty-first centuries (Zaxarenko, Komarova and Nečajeva 2003; Efremova 2000; Lopatin 2002; Skljarevskaja 1998). These dictionaries serve as a valuable source of information on vocabulary borrowed during the first decade of computerization and popularization of the Internet in Russia. The second source of data in the study is Russian-media texts taken from the Internet: popular blogs, online newspapers and other sites. Internet sites provide us with the most recent borrowings and with illustrations of the use of neologisms found in neologism dictionaries. The Internet data were analyzed with a text parsing program created specifically for this study. The program searched Russian language Internet sites for text contexts and various orthographic representations of new loan words. The study excluded emails and informal chat rooms so as to restrict the analysis to the most common loan words and discard occasionalisms. The study analyzes the structure of neologisms borrowed into Russian from English and other languages and discusses their impact on the morphological and orthographic structures of contemporary Russian.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".