Research on Cultural Identity of Marxist Chineseization Based on Extended Kalman Filter Algorithm
Bibliographic record
Abstract
The Chineseization of Marxism is one of the important topics of concern to Chinese social sciences.The study summarizes the main manifestations of the cultural identity of Marxist Chineseization, and estimates the potential growth rate of the Chinese economy using the extended Kalman filter algorithm from the dimension of material culture construction.Then based on CiteSpace, it conducts bibliometric measurements to explore the relationship between the Chineseization of Marxism and traditional Chinese culture.The measurement results of the model can better reflect the growth trend of the Chinese economy, and the economy will experience a period of medium-speed growth in the future, which should be seized to deepen the economic restructuring and promote the cultural identity of Marxist Chineseization by safeguarding the construction of material culture.The research literature on both the Chineseization of Marxism and traditional Chinese culture shows a general upward trend, especially from 2012-2021, with an increase of 3.06 times.The Chineseization of Marxism and Chinese culture have a deep-level fit, and the essence of Marxist ideology should be connected with the essence of Chinese traditional culture, so as to promote cultural identity and enhance cultural self-confidence in the process of the Chineseization of Marxism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".