Current Situation and Trend of the Research on the Consciousness of Community of the Chinese Nation: A Bibliometric Analysis Based on CSSCI Core Journals
Bibliographic record
Abstract
To firmly build the sense of community of the Chinese nation is the main line of all work carried out in ethnic areas in the new era, and is an important ideological project of socialism with Chinese characteristics in the new era. This paper selected 1967 CSSCI papers on the consciousness of community of the Chinese nation in Cast-in-place from 2017 to 2024 as the research data, and used CiteSpace 6.3.R1 software to make statistics on the number of publications, author analysis, keyword co-occurrence analysis, keyword cluster analysis, keyword emergence analysis, and distribution analysis of publishing institutions and funds. Draw a visual visual map to show the research status and trend of the Chinese nation's community consciousness. The analysis results show that: the national policy has a strong guidance for building the Chinese nation's sense of community; Ethnic areas and ethnic colleges and universities have great influence on the research of the consciousness of the Chinese nation community. The research on the consciousness of the Chinese nation community involves a wide range of disciplines. The degree of researcher cooperation is low; In the current research, "times value", "common prosperity", "cultural self-confidence", "moral cultivation" and "symbols" have been extensively studied, which is the hot content of the current consciousness of the Chinese national community.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.102 | 0.161 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".