Cultural Diversity in the World and Socialist Culture with Chinese Characteristics—Review of the Second World Cultural Forum
Bibliographic record
Abstract
The World Cultural Forum is a platform for scholars around the world to discuss crucial issues in relation to the cultural development, cross-cultural understanding and cooperation, and development of the cultural cause of the entire human race. With the theme of “Cultural Diversity in the World and Socialist Culture with Chinese Characteristics,” the Second World Cultural Forum was held on October 16, 2017, at the Chinese Academy of Social Sciences, Beijing, China. About 200 scholars from over 10 different countries, including Russia, Canada, Italy, the United States, the United Kingdom, Brazil, Ukraine, Bulgaria, Nigeria, and China, participated in the forum and exchanged ideas on the cultural challenges that are confronting China and the world today. Particularly, there were profound discussions on the substantial content of Marxist cultural theories, cultural progressiveness and the significance of the October Revolution to the development of world cultures, cultural diversity as a means to fight cultural imperialism, and the theory and practice of socialist culture with Chinese characteristics. A summary of the main points from these discussions is provided here for information and dissemination purposes.
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.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".