Morphology and Lexicology Interface. Latest Russian Neologisms: The Next Step towards Analytism?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it