Creating portraits of the cultures of countries in the holistic sense to enhance well-being and come to grips with the world’s most difficult problems
Bibliographic record
Abstract
The article explores the significance of portraying the holistic cultures of nations as a pathway to addressing the world’s complex problems and fostering human well-being. While culture is often narrowly defined through arts, humanities, and history, the paper emphasizes a broader, holistic perspective, viewing cultures as “complex wholes” or “total ways of life.” This comprehensive approach incorporates values, traditions, customs, symbols, and the dynamic interrelations among their elements. Using symbolic representations, such as iconic architecture or artistic works, the article argues for their profound ability to capture the essence of cultures, as illustrated by examples like the Eiffel Tower for France or Gandhi’s use of homespun cloth in India.It is highlighted the potential of cultural symbols to inspire unity and understanding amid global challenges like environmental crises, economic inequality, and social tensions in the article. Furthermore, it is shown the importance of transitioning from an economically driven worldview to a culturally enriched one, prioritizing harmony between material and spiritual values. Through the lens of specific case studies, including Spain's cultural depth, the article illuminates how nations can leverage arts, heritage, and collective cultural identity to shape more equitable, inclusive, and sustainable societies. This transformative vision emphasizes the role of governments, corporations, educational institutions, and individuals in cultivating cultures for a more harmonious global future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".