Niedzwiedzia Przysluga?Bear’s Favor? Hidden Garden behind the Concrete Proverbs: Cognitive-Semantic Analysis of Proverbs in Persian, Polish and Spanish
Bibliographic record
Abstract
Proverbs help us understand how the society works at large and what are the main concerns regarding the environment, people-to-people exchange and notions of liberty, freedom and values. In some cultures such as the Iranian one, the way one uses proverbs depends on the generation one finds herself in. Generation-gap provides opportunity to transfer some abstract and complicated concepts, not available in modern life, through the use of proverbs. From childhood, by hearing proverbs from parents and grand-parents, children begin grasping some important national and even religious concepts. In order to represent a rather international, holistic view and not language-specific, we analyzed further Polish, French and Spanish proverbs, whenever deemed necessary. The present paper through cognitive-semantic and content analysis aims to reveal the implied systems of value, ethics and morality realized through proverbs. The results clearly indicate that proverbs cover different systems of values through elements such as artifacts, animals, human body parts and even imaginary, nature-derived elements.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".