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
The relevance of the article is justified by the fact that answering the question on typical ways of expressing connotation in borrowed words and their connection with the history and ethnos development the presented article fills the research gap in linguistics. The objective of the article is to summarize the comparative study of semantic (primarily connotative) evolution of two words ‘orda’ (Mongolian ‘ordu’ Knan’s palace, headquarters) – ‘ulus’ borrowed from Mongolian. The principal method of the research involves semantic context analysis. The analysis consists of 3 stages. At the first stage the semantic analysis of the words in Russian National Corpus, precedent texts (proverbs, idioms, popular quotations etc.) and modern Russian dictionaries reveals the fixed connotations in the language and brings out stereotypical images in the Russian culture. The second stage focuses on Russians’ individual perceptions of the words and summarizes the results of an associative experiment – respondents’ reactions to the stimulus ‘orda’, ‘ulus’. Thirdly, the semantic network analysis of the words performed on modern social media texts (chats and forums) reveals the typical semantic context of the words represented by collocations. Based on the principles of explanation, expansionism, functionalism and anthropocentrism as the main principles of modern linguistics paradigm the article might be of interest to linguists pursing research in semantics, students majoring in Linguistics and Cultural studies and teachers of Russian.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it