EUROPEAN GENOCODES OF NATIONAL CULTURES. IN VARIETATE CONCORDIA
Bibliographic record
Abstract
This paper has summarized the foundations of the author’s interdisciplinary concept of national cultures genocodes (NCG), which represents synthesis of of the Hegelian doctrine about national spirits (Volksgeist), the Jung’s theory of the collective unconscious, system of hexagrams of the Ancient Chinese Book of Changes and Girt Hofstede’s measurements of national cultures. There are generic genes (archetypes) and species ones in the national genocodes. Generic genes are the cornerstone of 6 cultural kingdoms, and specific genes — separate national cultures. All countries measured by Hofstede are divided into 6 cultural kingdoms. Kingdoms of Qian, Creative (USA, Canada, Australia, Poland and Scandinavia), Lee, Radiance (Western Europe) and Gen, Keeping Still (Japan) have an individualistic genocode. Kingdoms Zhen, Arousing (Russia, the Balkans, Greece, Turkey, Central Asia), Dui, Joyous (Africa, Latin America, Middle East) and Kun, Receptive (China, India and Southeast Asia) have a collectivist genocode. The basis of European civilization is the generic genes of the kingdoms of Lee, Qian and Zhen. The interaction between the generic and species genes of these kingdoms, their complementarity or conflict lies at the heart of alliances or wars between states. It has been shown that the greatest conflicts are observed between countries with individualistic and collectivist cultures, therefore the Balkan countries, Greece, Turkey and Russia throughout history were in a varying degree unfavorable relations with the Western European countries. But studying NCG will allow to minimize tension between countries, to understand a role and the place of each country in the World and in particular the European Cultural Space, to lay a way to the long-term union of peoples and states.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".