Pejorative Connotation of Proverbs and Sayings with Zoonym in the Russian, German and Tatar Languages
Bibliographic record
Abstract
The problem of the interaction of language and culture is of interest to many scientists nowadays. Proverbs and sayings are units which contain bits of folk wisdom, values and beliefs of the nation. One of the ways to study a culture is to analyze its proverbs and sayings. The aim of the study was to compare paremiological units, namely proverbs and sayings, with zoonym components of three typologically unrelated languages: Russian, German and Tatar. The article deals with proverbs and sayings with the names of domestic animals only. In the study we used such methods as descriptive, structural, interpretative, continuous sampling method and statistical method. The analysis of the selected material revealed 847 Russian, 386 German and Tatar 1634 proverbs and sayings with the domestic animal components, 20 zoonyms in total, including names of birds. The study showed that paremiological units with the names of domestic animals in some cases carry the same connotative semes, mostly pejorative, in all three languages. However, the same component of proverbs in a particular language may have the opposite meaning depending on the speech situation. Such pejorative connotative semes as [stupidity, ignorance], [idleness, laziness], [cowardice], [greed] and etc. were revealed in numerous Russian, German and Tatar proverbs and sayings. The materials of the study may be used in cultural linguistics, cognitive linguistics, cultural studies and phraseology.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".